Overview

Dataset statistics

Number of variables47
Number of observations2156232
Missing cells23985531
Missing cells (%)23.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory773.2 MiB
Average record size in memory376.0 B

Variable types

Numeric11
DateTime4
Categorical11
Text20
Unsupported1

Alerts

Incident Zip is highly overall correlated with BBL and 7 other fieldsHigh correlation
BBL is highly overall correlated with Incident Zip and 8 other fieldsHigh correlation
X Coordinate (State Plane) is highly overall correlated with Incident Zip and 5 other fieldsHigh correlation
Y Coordinate (State Plane) is highly overall correlated with BBL and 6 other fieldsHigh correlation
Latitude is highly overall correlated with BBL and 6 other fieldsHigh correlation
Longitude is highly overall correlated with Incident Zip and 5 other fieldsHigh correlation
Zip Codes is highly overall correlated with Incident Zip and 8 other fieldsHigh correlation
Police Precincts is highly overall correlated with Incident Zip and 6 other fieldsHigh correlation
Agency is highly overall correlated with Agency Name and 3 other fieldsHigh correlation
Agency Name is highly overall correlated with Agency and 3 other fieldsHigh correlation
Facility Type is highly overall correlated with Incident Zip and 3 other fieldsHigh correlation
Borough is highly overall correlated with BBL and 9 other fieldsHigh correlation
Open Data Channel Type is highly overall correlated with Facility TypeHigh correlation
Park Borough is highly overall correlated with BBL and 9 other fieldsHigh correlation
Vehicle Type is highly overall correlated with Incident Zip and 2 other fieldsHigh correlation
Taxi Company Borough is highly overall correlated with Incident Zip and 12 other fieldsHigh correlation
Borough Boundaries is highly overall correlated with BBL and 9 other fieldsHigh correlation
Address Type is highly imbalanced (77.4%)Imbalance
Facility Type is highly imbalanced (59.3%)Imbalance
Status is highly imbalanced (82.3%)Imbalance
Vehicle Type is highly imbalanced (78.6%)Imbalance
Closed Date has 163693 (7.6%) missing valuesMissing
Descriptor has 30750 (1.4%) missing valuesMissing
Location Type has 256708 (11.9%) missing valuesMissing
Incident Zip has 27020 (1.3%) missing valuesMissing
Incident Address has 80531 (3.7%) missing valuesMissing
Street Name has 80593 (3.7%) missing valuesMissing
Cross Street 1 has 539647 (25.0%) missing valuesMissing
Cross Street 2 has 538899 (25.0%) missing valuesMissing
Intersection Street 1 has 635953 (29.5%) missing valuesMissing
Intersection Street 2 has 634646 (29.4%) missing valuesMissing
City has 119922 (5.6%) missing valuesMissing
Landmark has 797021 (37.0%) missing valuesMissing
Facility Type has 2018905 (93.6%) missing valuesMissing
Due Date has 2148125 (99.6%) missing valuesMissing
Resolution Description has 55803 (2.6%) missing valuesMissing
Resolution Action Updated Date has 51376 (2.4%) missing valuesMissing
BBL has 261151 (12.1%) missing valuesMissing
X Coordinate (State Plane) has 33541 (1.6%) missing valuesMissing
Y Coordinate (State Plane) has 32919 (1.5%) missing valuesMissing
Vehicle Type has 2155736 (> 99.9%) missing valuesMissing
Taxi Company Borough has 2155022 (99.9%) missing valuesMissing
Taxi Pick Up Location has 2132427 (98.9%) missing valuesMissing
Bridge Highway Name has 2140247 (99.3%) missing valuesMissing
Bridge Highway Direction has 2147873 (99.6%) missing valuesMissing
Road Ramp has 2151397 (99.8%) missing valuesMissing
Bridge Highway Segment has 2140241 (99.3%) missing valuesMissing
Latitude has 33616 (1.6%) missing valuesMissing
Longitude has 33616 (1.6%) missing valuesMissing
Location has 33616 (1.6%) missing valuesMissing
Zip Codes has 42923 (2.0%) missing valuesMissing
Community Districts has 34126 (1.6%) missing valuesMissing
Borough Boundaries has 34131 (1.6%) missing valuesMissing
City Council Districts has 34126 (1.6%) missing valuesMissing
Police Precincts has 34126 (1.6%) missing valuesMissing
Request Closing Time has 163693 (7.6%) missing valuesMissing
Unique Key has unique valuesUnique
Request Closing Time is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-11-08 16:07:57.294059
Analysis finished2023-11-08 16:11:13.748262
Duration3 minutes and 16.45 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

Unique Key
Real number (ℝ)

UNIQUE 

Distinct2156232
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58193077
Minimum57020601
Maximum59353779
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 MiB
2023-11-08T23:11:13.794777image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum57020601
5-th percentile57142676
Q157609028
median58194522
Q358776800
95-th percentile59239322
Maximum59353779
Range2333178
Interquartile range (IQR)1167772.5

Descriptive statistics

Standard deviation672836.97
Coefficient of variation (CV)0.011562148
Kurtosis-1.2036443
Mean58193077
Median Absolute Deviation (MAD)583831
Skewness-0.0041783373
Sum1.2547777 × 1014
Variance4.5270958 × 1011
MonotonicityNot monotonic
2023-11-08T23:11:13.855567image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59348005 1
 
< 0.1%
57804588 1
 
< 0.1%
57804295 1
 
< 0.1%
57802015 1
 
< 0.1%
57801375 1
 
< 0.1%
57802607 1
 
< 0.1%
57802125 1
 
< 0.1%
57802445 1
 
< 0.1%
57801568 1
 
< 0.1%
57800520 1
 
< 0.1%
Other values (2156222) 2156222
> 99.9%
ValueCountFrequency (%)
57020601 1
< 0.1%
57020602 1
< 0.1%
57020603 1
< 0.1%
57020606 1
< 0.1%
57020608 1
< 0.1%
57020611 1
< 0.1%
57020612 1
< 0.1%
57020613 1
< 0.1%
57020614 1
< 0.1%
57020622 1
< 0.1%
ValueCountFrequency (%)
59353779 1
< 0.1%
59353778 1
< 0.1%
59353777 1
< 0.1%
59353776 1
< 0.1%
59353775 1
< 0.1%
59353774 1
< 0.1%
59353773 1
< 0.1%
59353772 1
< 0.1%
59353771 1
< 0.1%
59353721 1
< 0.1%
Distinct1768322
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Memory size16.5 MiB
Minimum2023-03-12 18:51:46
Maximum2023-11-07 12:00:00
2023-11-08T23:11:13.913693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:11:13.969292image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Closed Date
Date

MISSING 

Distinct1489653
Distinct (%)74.8%
Missing163693
Missing (%)7.6%
Memory size16.5 MiB
Minimum2022-11-30 17:42:00
Maximum2023-11-08 22:00:00
2023-11-08T23:11:14.024015image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:11:14.080968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Agency
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.5 MiB
NYPD
974274 
HPD
376614 
DSNY
217308 
DOT
124456 
DEP
117798 
Other values (11)
345782 

Length

Max length44
Median length4
Mean length3.6197107
Min length3

Characters and Unicode

Total characters7804936
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDSNY
2nd rowDSNY
3rd rowDSNY
4th rowDOT
5th rowNYPD

Common Values

ValueCountFrequency (%)
NYPD 974274
45.2%
HPD 376614
 
17.5%
DSNY 217308
 
10.1%
DOT 124456
 
5.8%
DEP 117798
 
5.5%
DPR 105190
 
4.9%
DOB 68736
 
3.2%
DOHMH 59988
 
2.8%
DHS 37152
 
1.7%
EDC 35460
 
1.6%
Other values (6) 39256
 
1.8%

Length

2023-11-08T23:11:14.131966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nypd 974274
45.1%
hpd 376614
 
17.5%
dsny 217308
 
10.1%
dot 124456
 
5.8%
dep 117798
 
5.5%
dpr 105190
 
4.9%
dob 68736
 
3.2%
dohmh 59988
 
2.8%
dhs 37152
 
1.7%
edc 35460
 
1.6%
Other values (11) 41016
 
1.9%

Most occurring characters

ValueCountFrequency (%)
D 2132359
27.3%
P 1584830
20.3%
N 1192990
15.3%
Y 1191582
15.3%
H 533742
 
6.8%
O 255993
 
3.3%
S 254812
 
3.3%
E 156015
 
2.0%
T 150089
 
1.9%
R 106950
 
1.4%
Other values (11) 245574
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7803176
> 99.9%
Space Separator 1760
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 2132359
27.3%
P 1584830
20.3%
N 1192990
15.3%
Y 1191582
15.3%
H 533742
 
6.8%
O 255993
 
3.3%
S 254812
 
3.3%
E 156015
 
2.0%
T 150089
 
1.9%
R 106950
 
1.4%
Other values (10) 243814
 
3.1%
Space Separator
ValueCountFrequency (%)
1760
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7803176
> 99.9%
Common 1760
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 2132359
27.3%
P 1584830
20.3%
N 1192990
15.3%
Y 1191582
15.3%
H 533742
 
6.8%
O 255993
 
3.3%
S 254812
 
3.3%
E 156015
 
2.0%
T 150089
 
1.9%
R 106950
 
1.4%
Other values (10) 243814
 
3.1%
Common
ValueCountFrequency (%)
1760
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7804936
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 2132359
27.3%
P 1584830
20.3%
N 1192990
15.3%
Y 1191582
15.3%
H 533742
 
6.8%
O 255993
 
3.3%
S 254812
 
3.3%
E 156015
 
2.0%
T 150089
 
1.9%
R 106950
 
1.4%
Other values (11) 245574
 
3.1%

Agency Name
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.5 MiB
New York City Police Department
974274 
Department of Housing Preservation and Development
376614 
Department of Sanitation
217308 
Department of Transportation
124456 
Department of Environmental Protection
117798 
Other values (11)
345782 

Length

Max length50
Median length44
Mean length33.986435
Min length23

Characters and Unicode

Total characters73282638
Distinct characters40
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDepartment of Sanitation
2nd rowDepartment of Sanitation
3rd rowDepartment of Sanitation
4th rowDepartment of Transportation
5th rowNew York City Police Department

Common Values

ValueCountFrequency (%)
New York City Police Department 974274
45.2%
Department of Housing Preservation and Development 376614
 
17.5%
Department of Sanitation 217308
 
10.1%
Department of Transportation 124456
 
5.8%
Department of Environmental Protection 117798
 
5.5%
Department of Parks and Recreation 105190
 
4.9%
Department of Buildings 68736
 
3.2%
Department of Health and Mental Hygiene 59988
 
2.8%
Department of Homeless Services 37152
 
1.7%
Economic Development Corporation 35460
 
1.6%
Other values (6) 39256
 
1.8%

Length

2023-11-08T23:11:14.175863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
department 2096195
20.7%
of 1121977
11.1%
new 974274
9.6%
city 974274
9.6%
police 974274
9.6%
york 974274
9.6%
and 576267
 
5.7%
development 412074
 
4.1%
preservation 376614
 
3.7%
housing 376614
 
3.7%
Other values (26) 1294006
12.7%

Most occurring characters

ValueCountFrequency (%)
e 9023739
12.3%
7994611
10.9%
t 7274805
 
9.9%
o 5494299
 
7.5%
n 5431853
 
7.4%
r 4674873
 
6.4%
a 4245224
 
5.8%
i 4044210
 
5.5%
m 2785572
 
3.8%
p 2668537
 
3.6%
Other values (30) 19644915
26.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 56835428
77.6%
Uppercase Letter 8452599
 
11.5%
Space Separator 7994611
 
10.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 9023739
15.9%
t 7274805
12.8%
o 5494299
9.7%
n 5431853
9.6%
r 4674873
8.2%
a 4245224
7.5%
i 4044210
7.1%
m 2785572
 
4.9%
p 2668537
 
4.7%
l 1730418
 
3.0%
Other values (12) 9461898
16.6%
Uppercase Letter
ValueCountFrequency (%)
D 2508621
29.7%
P 1584126
18.7%
C 1048289
12.4%
N 974274
 
11.5%
Y 974274
 
11.5%
H 533742
 
6.3%
S 254460
 
3.0%
E 154255
 
1.8%
T 148681
 
1.8%
R 105190
 
1.2%
Other values (7) 166687
 
2.0%
Space Separator
ValueCountFrequency (%)
7994611
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 65288027
89.1%
Common 7994611
 
10.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 9023739
13.8%
t 7274805
11.1%
o 5494299
 
8.4%
n 5431853
 
8.3%
r 4674873
 
7.2%
a 4245224
 
6.5%
i 4044210
 
6.2%
m 2785572
 
4.3%
p 2668537
 
4.1%
D 2508621
 
3.8%
Other values (29) 17136294
26.2%
Common
ValueCountFrequency (%)
7994611
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73282638
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 9023739
12.3%
7994611
10.9%
t 7274805
 
9.9%
o 5494299
 
7.5%
n 5431853
 
7.4%
r 4674873
 
6.4%
a 4245224
 
5.8%
i 4044210
 
5.5%
m 2785572
 
3.8%
p 2668537
 
3.6%
Other values (30) 19644915
26.8%
Distinct192
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.5 MiB
2023-11-08T23:11:14.408234image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length39
Median length31
Mean length16.263301
Min length4

Characters and Unicode

Total characters35067451
Distinct characters56
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st rowDerelict Vehicles
2nd rowDerelict Vehicles
3rd rowDerelict Vehicles
4th rowStreet Condition
5th rowPanhandling
ValueCountFrequency (%)
noise 497496
 
10.7%
469661
 
10.1%
illegal 358185
 
7.7%
parking 319523
 
6.9%
condition 232766
 
5.0%
residential 215403
 
4.6%
water 155275
 
3.3%
street/sidewalk 125895
 
2.7%
blocked 108159
 
2.3%
driveway 108159
 
2.3%
Other values (249) 2071336
44.4%
2023-11-08T23:11:14.717354image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 3660737
 
10.4%
i 2814143
 
8.0%
2505626
 
7.1%
l 2209444
 
6.3%
a 1833101
 
5.2%
n 1710817
 
4.9%
o 1652360
 
4.7%
t 1539141
 
4.4%
r 1400510
 
4.0%
s 1286839
 
3.7%
Other values (46) 14454733
41.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23291573
66.4%
Uppercase Letter 8386045
 
23.9%
Space Separator 2505626
 
7.1%
Dash Punctuation 491995
 
1.4%
Other Punctuation 378574
 
1.1%
Open Punctuation 6819
 
< 0.1%
Close Punctuation 6819
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3660737
15.7%
i 2814143
12.1%
l 2209444
9.5%
a 1833101
7.9%
n 1710817
7.3%
o 1652360
 
7.1%
t 1539141
 
6.6%
r 1400510
 
6.0%
s 1286839
 
5.5%
g 929577
 
4.0%
Other values (16) 4254904
18.3%
Uppercase Letter
ValueCountFrequency (%)
N 1006834
12.0%
I 785935
 
9.4%
T 683240
 
8.1%
A 670063
 
8.0%
S 638768
 
7.6%
R 620717
 
7.4%
P 584643
 
7.0%
C 482809
 
5.8%
E 455104
 
5.4%
D 441842
 
5.3%
Other values (15) 2016090
24.0%
Space Separator
ValueCountFrequency (%)
2505626
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 491995
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 378574
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6819
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6819
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 31677618
90.3%
Common 3389833
 
9.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3660737
 
11.6%
i 2814143
 
8.9%
l 2209444
 
7.0%
a 1833101
 
5.8%
n 1710817
 
5.4%
o 1652360
 
5.2%
t 1539141
 
4.9%
r 1400510
 
4.4%
s 1286839
 
4.1%
N 1006834
 
3.2%
Other values (41) 12563692
39.7%
Common
ValueCountFrequency (%)
2505626
73.9%
- 491995
 
14.5%
/ 378574
 
11.2%
( 6819
 
0.2%
) 6819
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35067451
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3660737
 
10.4%
i 2814143
 
8.0%
2505626
 
7.1%
l 2209444
 
6.3%
a 1833101
 
5.2%
n 1710817
 
4.9%
o 1652360
 
4.7%
t 1539141
 
4.4%
r 1400510
 
4.0%
s 1286839
 
3.7%
Other values (46) 14454733
41.2%

Descriptor
Text

MISSING 

Distinct954
Distinct (%)< 0.1%
Missing30750
Missing (%)1.4%
Memory size16.5 MiB
2023-11-08T23:11:14.907421image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length80
Median length66
Mean length16.874297
Min length3

Characters and Unicode

Total characters35866014
Distinct characters73
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)< 0.1%

Sample

1st rowDerelict Vehicles
2nd rowDerelict Vehicles
3rd rowDerelict Vehicles
4th rowPothole
5th rowN/A
ValueCountFrequency (%)
loud 320032
 
6.3%
music/party 274081
 
5.4%
blocked 169062
 
3.4%
parking 112167
 
2.2%
hydrant 111193
 
2.2%
access 108161
 
2.1%
99031
 
2.0%
no 89701
 
1.8%
violation 83299
 
1.7%
sign 77292
 
1.5%
Other values (1258) 3597335
71.4%
2023-11-08T23:11:15.151866image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2916534
 
8.1%
e 2423526
 
6.8%
i 2149973
 
6.0%
o 1896746
 
5.3%
a 1801732
 
5.0%
r 1741036
 
4.9%
n 1730264
 
4.8%
t 1690081
 
4.7%
s 1404691
 
3.9%
c 1253910
 
3.5%
Other values (63) 16857521
47.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23081875
64.4%
Uppercase Letter 8628320
 
24.1%
Space Separator 2916534
 
8.1%
Other Punctuation 686172
 
1.9%
Open Punctuation 164488
 
0.5%
Close Punctuation 164488
 
0.5%
Dash Punctuation 114140
 
0.3%
Decimal Number 109997
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2423526
10.5%
i 2149973
 
9.3%
o 1896746
 
8.2%
a 1801732
 
7.8%
r 1741036
 
7.5%
n 1730264
 
7.5%
t 1690081
 
7.3%
s 1404691
 
6.1%
c 1253910
 
5.4%
d 1214120
 
5.3%
Other values (16) 5775796
25.0%
Uppercase Letter
ValueCountFrequency (%)
P 920445
 
10.7%
L 768660
 
8.9%
N 558815
 
6.5%
B 538023
 
6.2%
S 533975
 
6.2%
A 525541
 
6.1%
T 499315
 
5.8%
E 482146
 
5.6%
R 460168
 
5.3%
O 451534
 
5.2%
Other values (16) 2889698
33.5%
Decimal Number
ValueCountFrequency (%)
1 42557
38.7%
2 20730
18.8%
3 16181
 
14.7%
4 12668
 
11.5%
0 8694
 
7.9%
5 6207
 
5.6%
8 1109
 
1.0%
9 1063
 
1.0%
6 526
 
0.5%
7 262
 
0.2%
Other Punctuation
ValueCountFrequency (%)
/ 622212
90.7%
: 36783
 
5.4%
, 25678
 
3.7%
. 1362
 
0.2%
& 121
 
< 0.1%
" 10
 
< 0.1%
* 6
 
< 0.1%
Space Separator
ValueCountFrequency (%)
2916534
100.0%
Open Punctuation
ValueCountFrequency (%)
( 164488
100.0%
Close Punctuation
ValueCountFrequency (%)
) 164488
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 114140
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 31710195
88.4%
Common 4155819
 
11.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2423526
 
7.6%
i 2149973
 
6.8%
o 1896746
 
6.0%
a 1801732
 
5.7%
r 1741036
 
5.5%
n 1730264
 
5.5%
t 1690081
 
5.3%
s 1404691
 
4.4%
c 1253910
 
4.0%
d 1214120
 
3.8%
Other values (42) 14404116
45.4%
Common
ValueCountFrequency (%)
2916534
70.2%
/ 622212
 
15.0%
( 164488
 
4.0%
) 164488
 
4.0%
- 114140
 
2.7%
1 42557
 
1.0%
: 36783
 
0.9%
, 25678
 
0.6%
2 20730
 
0.5%
3 16181
 
0.4%
Other values (11) 32028
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35866014
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2916534
 
8.1%
e 2423526
 
6.8%
i 2149973
 
6.0%
o 1896746
 
5.3%
a 1801732
 
5.0%
r 1741036
 
4.9%
n 1730264
 
4.8%
t 1690081
 
4.7%
s 1404691
 
3.9%
c 1253910
 
3.5%
Other values (63) 16857521
47.0%

Location Type
Text

MISSING 

Distinct147
Distinct (%)< 0.1%
Missing256708
Missing (%)11.9%
Memory size16.5 MiB
2023-11-08T23:11:15.320659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length36
Median length30
Mean length15.615429
Min length3

Characters and Unicode

Total characters29661882
Distinct characters58
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowStreet
2nd rowStreet
3rd rowStreet
4th rowSubway
5th rowStreet/Sidewalk
ValueCountFrequency (%)
street/sidewalk 695397
26.1%
residential 607588
22.8%
building 404790
15.2%
street 250836
 
9.4%
building/house 227096
 
8.5%
sidewalk 99034
 
3.7%
store/commercial 37038
 
1.4%
above 35458
 
1.3%
address 35458
 
1.3%
family 29291
 
1.1%
Other values (166) 238878
 
9.0%
2023-11-08T23:11:15.542269image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 3656769
 
12.3%
t 2270212
 
7.7%
S 2172541
 
7.3%
i 1902191
 
6.4%
I 1506784
 
5.1%
l 1434948
 
4.8%
d 1384470
 
4.7%
a 1293105
 
4.4%
r 1217254
 
4.1%
/ 1046003
 
3.5%
Other values (48) 11777605
39.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 17681607
59.6%
Uppercase Letter 10078370
34.0%
Other Punctuation 1060120
 
3.6%
Space Separator 761340
 
2.6%
Decimal Number 37863
 
0.1%
Math Symbol 20777
 
0.1%
Dash Punctuation 8863
 
< 0.1%
Open Punctuation 6471
 
< 0.1%
Close Punctuation 6471
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3656769
20.7%
t 2270212
12.8%
i 1902191
10.8%
l 1434948
 
8.1%
d 1384470
 
7.8%
a 1293105
 
7.3%
r 1217254
 
6.9%
k 840891
 
4.8%
w 824925
 
4.7%
s 616029
 
3.5%
Other values (14) 2240813
12.7%
Uppercase Letter
ValueCountFrequency (%)
S 2172541
21.6%
I 1506784
15.0%
D 770036
 
7.6%
E 759656
 
7.5%
L 756334
 
7.5%
N 753488
 
7.5%
B 693234
 
6.9%
R 639255
 
6.3%
A 469106
 
4.7%
U 384181
 
3.8%
Other values (13) 1173755
11.6%
Other Punctuation
ValueCountFrequency (%)
/ 1046003
98.7%
. 14115
 
1.3%
' 2
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
3 20842
55.0%
1 8543
22.6%
2 8478
22.4%
Space Separator
ValueCountFrequency (%)
761340
100.0%
Math Symbol
ValueCountFrequency (%)
+ 20777
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8863
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6471
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6471
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 27759977
93.6%
Common 1901905
 
6.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3656769
 
13.2%
t 2270212
 
8.2%
S 2172541
 
7.8%
i 1902191
 
6.9%
I 1506784
 
5.4%
l 1434948
 
5.2%
d 1384470
 
5.0%
a 1293105
 
4.7%
r 1217254
 
4.4%
k 840891
 
3.0%
Other values (37) 10080812
36.3%
Common
ValueCountFrequency (%)
/ 1046003
55.0%
761340
40.0%
3 20842
 
1.1%
+ 20777
 
1.1%
. 14115
 
0.7%
- 8863
 
0.5%
1 8543
 
0.4%
2 8478
 
0.4%
( 6471
 
0.3%
) 6471
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29661882
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3656769
 
12.3%
t 2270212
 
7.7%
S 2172541
 
7.3%
i 1902191
 
6.4%
I 1506784
 
5.1%
l 1434948
 
4.8%
d 1384470
 
4.7%
a 1293105
 
4.4%
r 1217254
 
4.1%
/ 1046003
 
3.5%
Other values (48) 11777605
39.7%

Incident Zip
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct348
Distinct (%)< 0.1%
Missing27020
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean10824.947
Minimum83
Maximum98057
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 MiB
2023-11-08T23:11:15.608303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum83
5-th percentile10014
Q110314
median11204
Q311235
95-th percentile11421
Maximum98057
Range97974
Interquartile range (IQR)921

Descriptive statistics

Standard deviation582.63775
Coefficient of variation (CV)0.053823611
Kurtosis1792.3152
Mean10824.947
Median Absolute Deviation (MAD)216
Skewness14.265663
Sum2.3048608 × 1010
Variance339466.75
MonotonicityNot monotonic
2023-11-08T23:11:15.663846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11226 32214
 
1.5%
11385 30213
 
1.4%
10467 29812
 
1.4%
11201 29122
 
1.4%
10452 28966
 
1.3%
10468 28517
 
1.3%
10456 27647
 
1.3%
10457 27632
 
1.3%
11207 27550
 
1.3%
11208 25684
 
1.2%
Other values (338) 1841855
85.4%
(Missing) 27020
 
1.3%
ValueCountFrequency (%)
83 2
< 0.1%
2062 2
< 0.1%
3833 1
< 0.1%
7002 1
< 0.1%
7017 1
< 0.1%
7052 1
< 0.1%
7072 1
< 0.1%
7080 1
< 0.1%
7083 1
< 0.1%
7104 1
< 0.1%
ValueCountFrequency (%)
98057 2
< 0.1%
95834 1
< 0.1%
94804 1
< 0.1%
91302 1
< 0.1%
84117 1
< 0.1%
82001 1
< 0.1%
78758 2
< 0.1%
75007 1
< 0.1%
75001 1
< 0.1%
60604 1
< 0.1%

Incident Address
Text

MISSING 

Distinct451975
Distinct (%)21.8%
Missing80531
Missing (%)3.7%
Memory size16.5 MiB
2023-11-08T23:11:15.920672image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length88
Median length51
Mean length17.810209
Min length1

Characters and Unicode

Total characters36968668
Distinct characters73
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique218095 ?
Unique (%)10.5%

Sample

1st row585 BRISTOL STREET
2nd row2362 EAST 13 STREET
3rd row34 HILLSIDE AVENUE
4th rowCRESCENT STREET
5th row637 EAST 230 STREET
ValueCountFrequency (%)
street 903176
 
13.7%
avenue 797956
 
12.1%
east 206310
 
3.1%
west 174541
 
2.6%
boulevard 73339
 
1.1%
place 69854
 
1.1%
road 67883
 
1.0%
park 31620
 
0.5%
broadway 29493
 
0.4%
parkway 25542
 
0.4%
Other values (33245) 4235393
64.0%
2023-11-08T23:11:16.211548image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5157816
14.0%
E 4881406
 
13.2%
T 2759937
 
7.5%
A 2117757
 
5.7%
R 1930669
 
5.2%
S 1838185
 
5.0%
1 1714844
 
4.6%
N 1620544
 
4.4%
2 1114775
 
3.0%
U 1107601
 
3.0%
Other values (63) 12725134
34.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 22739083
61.5%
Decimal Number 8618360
 
23.3%
Space Separator 5157816
 
14.0%
Dash Punctuation 447491
 
1.2%
Lowercase Letter 4854
 
< 0.1%
Other Punctuation 1059
 
< 0.1%
Close Punctuation 2
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 4881406
21.5%
T 2759937
12.1%
A 2117757
9.3%
R 1930669
 
8.5%
S 1838185
 
8.1%
N 1620544
 
7.1%
U 1107601
 
4.9%
V 981905
 
4.3%
O 907247
 
4.0%
L 655855
 
2.9%
Other values (16) 3937977
17.3%
Lowercase Letter
ValueCountFrequency (%)
e 640
13.2%
t 482
9.9%
r 474
9.8%
a 467
9.6%
n 378
 
7.8%
o 361
 
7.4%
s 281
 
5.8%
d 242
 
5.0%
i 196
 
4.0%
l 189
 
3.9%
Other values (16) 1144
23.6%
Decimal Number
ValueCountFrequency (%)
1 1714844
19.9%
2 1114775
12.9%
0 890258
10.3%
3 870816
10.1%
5 843257
9.8%
4 790347
9.2%
6 649003
 
7.5%
7 624882
 
7.3%
8 590150
 
6.8%
9 530028
 
6.1%
Other Punctuation
ValueCountFrequency (%)
' 638
60.2%
/ 393
37.1%
. 14
 
1.3%
, 11
 
1.0%
@ 2
 
0.2%
# 1
 
0.1%
Space Separator
ValueCountFrequency (%)
5157816
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 447491
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 22743937
61.5%
Common 14224731
38.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 4881406
21.5%
T 2759937
12.1%
A 2117757
9.3%
R 1930669
 
8.5%
S 1838185
 
8.1%
N 1620544
 
7.1%
U 1107601
 
4.9%
V 981905
 
4.3%
O 907247
 
4.0%
L 655855
 
2.9%
Other values (42) 3942831
17.3%
Common
ValueCountFrequency (%)
5157816
36.3%
1 1714844
 
12.1%
2 1114775
 
7.8%
0 890258
 
6.3%
3 870816
 
6.1%
5 843257
 
5.9%
4 790347
 
5.6%
6 649003
 
4.6%
7 624882
 
4.4%
8 590150
 
4.1%
Other values (11) 978583
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36968668
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5157816
14.0%
E 4881406
 
13.2%
T 2759937
 
7.5%
A 2117757
 
5.7%
R 1930669
 
5.2%
S 1838185
 
5.0%
1 1714844
 
4.6%
N 1620544
 
4.4%
2 1114775
 
3.0%
U 1107601
 
3.0%
Other values (63) 12725134
34.4%

Street Name
Text

MISSING 

Distinct11264
Distinct (%)0.5%
Missing80593
Missing (%)3.7%
Memory size16.5 MiB
2023-11-08T23:11:16.445756image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length70
Median length47
Mean length13.410296
Min length2

Characters and Unicode

Total characters27834934
Distinct characters73
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1982 ?
Unique (%)0.1%

Sample

1st rowBRISTOL STREET
2nd rowEAST 13 STREET
3rd rowHILLSIDE AVENUE
4th rowCRESCENT STREET
5th rowEAST 230 STREET
ValueCountFrequency (%)
street 903174
 
19.4%
avenue 797952
 
17.1%
east 206292
 
4.4%
west 174540
 
3.7%
boulevard 73339
 
1.6%
place 69854
 
1.5%
road 67883
 
1.5%
park 31613
 
0.7%
broadway 29491
 
0.6%
parkway 25542
 
0.5%
Other values (5884) 2287334
49.0%
2023-11-08T23:11:16.743435image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 4880887
17.5%
3209785
11.5%
T 2759873
9.9%
A 2113179
 
7.6%
R 1930523
 
6.9%
S 1838126
 
6.6%
N 1620409
 
5.8%
U 1107591
 
4.0%
V 981881
 
3.5%
O 907168
 
3.3%
Other values (63) 6485512
23.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 22731109
81.7%
Space Separator 3209785
 
11.5%
Decimal Number 1889374
 
6.8%
Lowercase Letter 3843
 
< 0.1%
Other Punctuation 687
 
< 0.1%
Dash Punctuation 131
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 4880887
21.5%
T 2759873
12.1%
A 2113179
9.3%
R 1930523
 
8.5%
S 1838126
 
8.1%
N 1620409
 
7.1%
U 1107591
 
4.9%
V 981881
 
4.3%
O 907168
 
4.0%
L 655844
 
2.9%
Other values (16) 3935628
17.3%
Lowercase Letter
ValueCountFrequency (%)
e 553
14.4%
t 400
10.4%
r 344
 
9.0%
a 340
 
8.8%
o 293
 
7.6%
n 292
 
7.6%
s 231
 
6.0%
d 181
 
4.7%
i 156
 
4.1%
u 154
 
4.0%
Other values (16) 899
23.4%
Decimal Number
ValueCountFrequency (%)
1 445566
23.6%
2 225880
12.0%
3 188638
10.0%
4 169179
 
9.0%
5 160877
 
8.5%
7 159842
 
8.5%
6 146560
 
7.8%
8 146120
 
7.7%
9 131451
 
7.0%
0 115261
 
6.1%
Other Punctuation
ValueCountFrequency (%)
' 638
92.9%
/ 31
 
4.5%
. 9
 
1.3%
, 7
 
1.0%
# 1
 
0.1%
@ 1
 
0.1%
Space Separator
ValueCountFrequency (%)
3209785
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 131
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 22734952
81.7%
Common 5099982
 
18.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 4880887
21.5%
T 2759873
12.1%
A 2113179
9.3%
R 1930523
 
8.5%
S 1838126
 
8.1%
N 1620409
 
7.1%
U 1107591
 
4.9%
V 981881
 
4.3%
O 907168
 
4.0%
L 655844
 
2.9%
Other values (42) 3939471
17.3%
Common
ValueCountFrequency (%)
3209785
62.9%
1 445566
 
8.7%
2 225880
 
4.4%
3 188638
 
3.7%
4 169179
 
3.3%
5 160877
 
3.2%
7 159842
 
3.1%
6 146560
 
2.9%
8 146120
 
2.9%
9 131451
 
2.6%
Other values (11) 116084
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27834934
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 4880887
17.5%
3209785
11.5%
T 2759873
9.9%
A 2113179
 
7.6%
R 1930523
 
6.9%
S 1838126
 
6.6%
N 1620409
 
5.8%
U 1107591
 
4.0%
V 981881
 
3.5%
O 907168
 
3.3%
Other values (63) 6485512
23.3%

Cross Street 1
Text

MISSING 

Distinct15985
Distinct (%)1.0%
Missing539647
Missing (%)25.0%
Memory size16.5 MiB
2023-11-08T23:11:16.953642image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length33
Median length30
Mean length12.717315
Min length2

Characters and Unicode

Total characters20558620
Distinct characters65
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2450 ?
Unique (%)0.2%

Sample

1st rowLOTT AVENUE
2nd rowGRAVESEND NECK ROAD
3rd rowBOGARDUS PLACE
4th row23 AVENUE
5th rowCARPENTER AVENUE
ValueCountFrequency (%)
avenue 687841
 
19.0%
street 531633
 
14.7%
east 126373
 
3.5%
west 99332
 
2.7%
road 58886
 
1.6%
st 57238
 
1.6%
place 53827
 
1.5%
ave 47567
 
1.3%
boulevard 46448
 
1.3%
broadway 23276
 
0.6%
Other values (5642) 1891794
52.2%
2023-11-08T23:11:17.220878image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 3573253
17.4%
2316211
11.3%
T 1774615
 
8.6%
A 1716435
 
8.3%
N 1299697
 
6.3%
R 1290307
 
6.3%
S 1187987
 
5.8%
U 915040
 
4.5%
V 861292
 
4.2%
O 641208
 
3.1%
Other values (55) 4982575
24.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 16759002
81.5%
Space Separator 2316211
 
11.3%
Decimal Number 1476750
 
7.2%
Dash Punctuation 3228
 
< 0.1%
Other Punctuation 3079
 
< 0.1%
Lowercase Letter 272
 
< 0.1%
Open Punctuation 39
 
< 0.1%
Close Punctuation 39
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 3573253
21.3%
T 1774615
10.6%
A 1716435
10.2%
N 1299697
 
7.8%
R 1290307
 
7.7%
S 1187987
 
7.1%
U 915040
 
5.5%
V 861292
 
5.1%
O 641208
 
3.8%
L 530024
 
3.2%
Other values (16) 2969144
17.7%
Lowercase Letter
ValueCountFrequency (%)
t 48
17.6%
i 46
16.9%
x 35
12.9%
l 24
8.8%
r 19
 
7.0%
e 18
 
6.6%
v 13
 
4.8%
n 10
 
3.7%
a 10
 
3.7%
d 8
 
2.9%
Other values (11) 41
15.1%
Decimal Number
ValueCountFrequency (%)
1 341635
23.1%
2 161860
11.0%
3 141481
9.6%
8 134632
 
9.1%
4 124402
 
8.4%
5 124068
 
8.4%
7 123654
 
8.4%
6 115896
 
7.8%
9 105203
 
7.1%
0 103919
 
7.0%
Other Punctuation
ValueCountFrequency (%)
? 1716
55.7%
/ 810
26.3%
' 521
 
16.9%
& 32
 
1.0%
Space Separator
ValueCountFrequency (%)
2316211
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3228
100.0%
Open Punctuation
ValueCountFrequency (%)
( 39
100.0%
Close Punctuation
ValueCountFrequency (%)
) 39
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16759274
81.5%
Common 3799346
 
18.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 3573253
21.3%
T 1774615
10.6%
A 1716435
10.2%
N 1299697
 
7.8%
R 1290307
 
7.7%
S 1187987
 
7.1%
U 915040
 
5.5%
V 861292
 
5.1%
O 641208
 
3.8%
L 530024
 
3.2%
Other values (37) 2969416
17.7%
Common
ValueCountFrequency (%)
2316211
61.0%
1 341635
 
9.0%
2 161860
 
4.3%
3 141481
 
3.7%
8 134632
 
3.5%
4 124402
 
3.3%
5 124068
 
3.3%
7 123654
 
3.3%
6 115896
 
3.1%
9 105203
 
2.8%
Other values (8) 110304
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20558620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 3573253
17.4%
2316211
11.3%
T 1774615
 
8.6%
A 1716435
 
8.3%
N 1299697
 
6.3%
R 1290307
 
6.3%
S 1187987
 
5.8%
U 915040
 
4.5%
V 861292
 
4.2%
O 641208
 
3.1%
Other values (55) 4982575
24.2%

Cross Street 2
Text

MISSING 

Distinct15959
Distinct (%)1.0%
Missing538899
Missing (%)25.0%
Memory size16.5 MiB
2023-11-08T23:11:17.452242image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length33
Median length29
Mean length12.970895
Min length1

Characters and Unicode

Total characters20978257
Distinct characters67
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2466 ?
Unique (%)0.2%

Sample

1st rowHEGEMAN AVENUE
2nd rowAVENUE X
3rd rowELLWOOD STREET
4th rowDITMARS BOULEVARD
5th rowLOWERRE PLACE
ValueCountFrequency (%)
avenue 662056
 
18.1%
street 548494
 
15.0%
east 131492
 
3.6%
west 104848
 
2.9%
road 63742
 
1.7%
boulevard 51986
 
1.4%
place 49884
 
1.4%
st 48659
 
1.3%
ave 45191
 
1.2%
broadway 19585
 
0.5%
Other values (5527) 1929583
52.8%
2023-11-08T23:11:17.737128image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 3613685
17.2%
2378467
11.3%
T 1828504
 
8.7%
A 1706950
 
8.1%
R 1354117
 
6.5%
N 1299169
 
6.2%
S 1233871
 
5.9%
U 901351
 
4.3%
V 861329
 
4.1%
O 686275
 
3.3%
Other values (57) 5114539
24.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 17122060
81.6%
Space Separator 2378467
 
11.3%
Decimal Number 1469159
 
7.0%
Dash Punctuation 4271
 
< 0.1%
Other Punctuation 3909
 
< 0.1%
Lowercase Letter 311
 
< 0.1%
Open Punctuation 40
 
< 0.1%
Close Punctuation 40
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 3613685
21.1%
T 1828504
10.7%
A 1706950
10.0%
R 1354117
 
7.9%
N 1299169
 
7.6%
S 1233871
 
7.2%
U 901351
 
5.3%
V 861329
 
5.0%
O 686275
 
4.0%
L 536211
 
3.1%
Other values (16) 3100598
18.1%
Lowercase Letter
ValueCountFrequency (%)
t 59
19.0%
i 41
13.2%
x 35
11.3%
e 21
 
6.8%
r 17
 
5.5%
l 16
 
5.1%
o 16
 
5.1%
a 14
 
4.5%
k 12
 
3.9%
n 12
 
3.9%
Other values (13) 68
21.9%
Decimal Number
ValueCountFrequency (%)
1 357946
24.4%
2 166316
11.3%
3 136011
 
9.3%
7 128621
 
8.8%
4 123469
 
8.4%
8 122564
 
8.3%
5 114301
 
7.8%
6 111245
 
7.6%
9 105475
 
7.2%
0 103211
 
7.0%
Other Punctuation
ValueCountFrequency (%)
/ 3038
77.7%
' 504
 
12.9%
? 364
 
9.3%
& 3
 
0.1%
Space Separator
ValueCountFrequency (%)
2378467
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4271
100.0%
Open Punctuation
ValueCountFrequency (%)
( 40
100.0%
Close Punctuation
ValueCountFrequency (%)
) 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 17122371
81.6%
Common 3855886
 
18.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 3613685
21.1%
T 1828504
10.7%
A 1706950
10.0%
R 1354117
 
7.9%
N 1299169
 
7.6%
S 1233871
 
7.2%
U 901351
 
5.3%
V 861329
 
5.0%
O 686275
 
4.0%
L 536211
 
3.1%
Other values (39) 3100909
18.1%
Common
ValueCountFrequency (%)
2378467
61.7%
1 357946
 
9.3%
2 166316
 
4.3%
3 136011
 
3.5%
7 128621
 
3.3%
4 123469
 
3.2%
8 122564
 
3.2%
5 114301
 
3.0%
6 111245
 
2.9%
9 105475
 
2.7%
Other values (8) 111471
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20978257
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 3613685
17.2%
2378467
11.3%
T 1828504
 
8.7%
A 1706950
 
8.1%
R 1354117
 
6.5%
N 1299169
 
6.2%
S 1233871
 
5.9%
U 901351
 
4.3%
V 861329
 
4.1%
O 686275
 
3.3%
Other values (57) 5114539
24.4%

Intersection Street 1
Text

MISSING 

Distinct10177
Distinct (%)0.7%
Missing635953
Missing (%)29.5%
Memory size16.5 MiB
2023-11-08T23:11:17.964555image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length41
Median length30
Mean length12.993419
Min length3

Characters and Unicode

Total characters19753622
Distinct characters61
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1322 ?
Unique (%)0.1%

Sample

1st rowCARPENTER AVENUE
2nd rowWEST 16 STREET
3rd rowAVENUE X
4th rowAMSTERDAM AVENUE
5th rowMCCLELLAN STREET
ValueCountFrequency (%)
avenue 691312
 
20.3%
street 533551
 
15.7%
east 126781
 
3.7%
west 100046
 
2.9%
road 59347
 
1.7%
place 53615
 
1.6%
boulevard 48982
 
1.4%
broadway 22952
 
0.7%
park 21927
 
0.6%
5 20242
 
0.6%
Other values (5274) 1729822
50.7%
2023-11-08T23:11:18.437618image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 3486229
17.6%
2212158
11.2%
T 1721138
 
8.7%
A 1641329
 
8.3%
N 1263288
 
6.4%
R 1256520
 
6.4%
S 1134391
 
5.7%
U 913367
 
4.6%
V 814043
 
4.1%
O 614901
 
3.1%
Other values (51) 4696258
23.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 16151747
81.8%
Space Separator 2212158
 
11.2%
Decimal Number 1385143
 
7.0%
Dash Punctuation 2980
 
< 0.1%
Other Punctuation 1383
 
< 0.1%
Lowercase Letter 207
 
< 0.1%
Close Punctuation 2
 
< 0.1%
Open Punctuation 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 3486229
21.6%
T 1721138
10.7%
A 1641329
10.2%
N 1263288
 
7.8%
R 1256520
 
7.8%
S 1134391
 
7.0%
U 913367
 
5.7%
V 814043
 
5.0%
O 614901
 
3.8%
L 498477
 
3.1%
Other values (16) 2808064
17.4%
Lowercase Letter
ValueCountFrequency (%)
o 29
14.0%
u 24
11.6%
n 24
11.6%
t 22
10.6%
d 20
9.7%
b 17
8.2%
s 16
7.7%
e 11
 
5.3%
a 10
 
4.8%
r 6
 
2.9%
Other values (8) 28
13.5%
Decimal Number
ValueCountFrequency (%)
1 319227
23.0%
2 151930
11.0%
3 133753
9.7%
8 126625
 
9.1%
5 116230
 
8.4%
7 115634
 
8.3%
4 115187
 
8.3%
6 109416
 
7.9%
9 99451
 
7.2%
0 97690
 
7.1%
Other Punctuation
ValueCountFrequency (%)
/ 930
67.2%
' 421
30.4%
& 32
 
2.3%
Space Separator
ValueCountFrequency (%)
2212158
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2980
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16151954
81.8%
Common 3601668
 
18.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 3486229
21.6%
T 1721138
10.7%
A 1641329
10.2%
N 1263288
 
7.8%
R 1256520
 
7.8%
S 1134391
 
7.0%
U 913367
 
5.7%
V 814043
 
5.0%
O 614901
 
3.8%
L 498477
 
3.1%
Other values (34) 2808271
17.4%
Common
ValueCountFrequency (%)
2212158
61.4%
1 319227
 
8.9%
2 151930
 
4.2%
3 133753
 
3.7%
8 126625
 
3.5%
5 116230
 
3.2%
7 115634
 
3.2%
4 115187
 
3.2%
6 109416
 
3.0%
9 99451
 
2.8%
Other values (7) 102057
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19753622
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 3486229
17.6%
2212158
11.2%
T 1721138
 
8.7%
A 1641329
 
8.3%
N 1263288
 
6.4%
R 1256520
 
6.4%
S 1134391
 
5.7%
U 913367
 
4.6%
V 814043
 
4.1%
O 614901
 
3.1%
Other values (51) 4696258
23.8%

Intersection Street 2
Text

MISSING 

Distinct10501
Distinct (%)0.7%
Missing634646
Missing (%)29.4%
Memory size16.5 MiB
2023-11-08T23:11:18.659408image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length37
Median length30
Mean length13.26327
Min length3

Characters and Unicode

Total characters20181206
Distinct characters60
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1416 ?
Unique (%)0.1%

Sample

1st rowLOWERRE PLACE
2nd rowPRIVATE CATANZARO SQUARE
3rd rowAVENUE Y
4th rowBROADWAY
5th rowEAST 167 STREET
ValueCountFrequency (%)
avenue 656512
 
19.0%
street 562824
 
16.3%
east 135741
 
3.9%
west 108557
 
3.1%
road 63167
 
1.8%
boulevard 52905
 
1.5%
place 49763
 
1.4%
drive 19029
 
0.6%
broadway 18392
 
0.5%
parkway 18034
 
0.5%
Other values (5382) 1763351
51.1%
2023-11-08T23:11:18.941138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 3536797
17.5%
2280702
11.3%
T 1802375
 
8.9%
A 1620638
 
8.0%
R 1324238
 
6.6%
N 1249806
 
6.2%
S 1195936
 
5.9%
U 885956
 
4.4%
V 803988
 
4.0%
O 654836
 
3.2%
Other values (50) 4825934
23.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 16491303
81.7%
Space Separator 2280702
 
11.3%
Decimal Number 1401426
 
6.9%
Dash Punctuation 4084
 
< 0.1%
Other Punctuation 3492
 
< 0.1%
Lowercase Letter 153
 
< 0.1%
Open Punctuation 23
 
< 0.1%
Close Punctuation 23
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 3536797
21.4%
T 1802375
10.9%
A 1620638
9.8%
R 1324238
 
8.0%
N 1249806
 
7.6%
S 1195936
 
7.3%
U 885956
 
5.4%
V 803988
 
4.9%
O 654836
 
4.0%
L 502203
 
3.0%
Other values (16) 2914530
17.7%
Lowercase Letter
ValueCountFrequency (%)
t 29
19.0%
i 22
14.4%
e 20
13.1%
x 17
11.1%
s 8
 
5.2%
r 8
 
5.2%
v 8
 
5.2%
l 8
 
5.2%
a 8
 
5.2%
n 6
 
3.9%
Other values (8) 19
12.4%
Decimal Number
ValueCountFrequency (%)
1 341300
24.4%
2 157458
11.2%
3 130002
 
9.3%
7 122383
 
8.7%
4 117117
 
8.4%
8 116715
 
8.3%
5 109728
 
7.8%
6 106365
 
7.6%
9 101381
 
7.2%
0 98977
 
7.1%
Other Punctuation
ValueCountFrequency (%)
/ 3061
87.7%
' 431
 
12.3%
Space Separator
ValueCountFrequency (%)
2280702
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4084
100.0%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%
Close Punctuation
ValueCountFrequency (%)
) 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16491456
81.7%
Common 3689750
 
18.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 3536797
21.4%
T 1802375
10.9%
A 1620638
9.8%
R 1324238
 
8.0%
N 1249806
 
7.6%
S 1195936
 
7.3%
U 885956
 
5.4%
V 803988
 
4.9%
O 654836
 
4.0%
L 502203
 
3.0%
Other values (34) 2914683
17.7%
Common
ValueCountFrequency (%)
2280702
61.8%
1 341300
 
9.2%
2 157458
 
4.3%
3 130002
 
3.5%
7 122383
 
3.3%
4 117117
 
3.2%
8 116715
 
3.2%
5 109728
 
3.0%
6 106365
 
2.9%
9 101381
 
2.7%
Other values (6) 106599
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20181206
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 3536797
17.5%
2280702
11.3%
T 1802375
 
8.9%
A 1620638
 
8.0%
R 1324238
 
6.6%
N 1249806
 
6.2%
S 1195936
 
5.9%
U 885956
 
4.4%
V 803988
 
4.0%
O 654836
 
3.2%
Other values (50) 4825934
23.9%

Address Type
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing11412
Missing (%)0.5%
Memory size16.5 MiB
ADDRESS
1951902 
INTERSECTION
 
152190
BLOCKFACE
 
23440
UNRECOGNIZED
 
15827
PLACENAME
 
1461

Length

Max length12
Median length7
Mean length7.4149006
Min length7

Characters and Unicode

Total characters15903627
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowADDRESS
2nd rowADDRESS
3rd rowADDRESS
4th rowBLOCKFACE
5th rowADDRESS

Common Values

ValueCountFrequency (%)
ADDRESS 1951902
90.5%
INTERSECTION 152190
 
7.1%
BLOCKFACE 23440
 
1.1%
UNRECOGNIZED 15827
 
0.7%
PLACENAME 1461
 
0.1%
(Missing) 11412
 
0.5%

Length

2023-11-08T23:11:19.007383image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-08T23:11:19.069522image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
address 1951902
91.0%
intersection 152190
 
7.1%
blockface 23440
 
1.1%
unrecognized 15827
 
0.7%
placename 1461
 
0.1%

Most occurring characters

ValueCountFrequency (%)
S 4055994
25.5%
D 3919631
24.6%
E 2314298
14.6%
R 2119919
13.3%
A 1978264
12.4%
N 337495
 
2.1%
I 320207
 
2.0%
T 304380
 
1.9%
C 216358
 
1.4%
O 191457
 
1.2%
Other values (9) 145624
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 15903627
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 4055994
25.5%
D 3919631
24.6%
E 2314298
14.6%
R 2119919
13.3%
A 1978264
12.4%
N 337495
 
2.1%
I 320207
 
2.0%
T 304380
 
1.9%
C 216358
 
1.4%
O 191457
 
1.2%
Other values (9) 145624
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 15903627
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 4055994
25.5%
D 3919631
24.6%
E 2314298
14.6%
R 2119919
13.3%
A 1978264
12.4%
N 337495
 
2.1%
I 320207
 
2.0%
T 304380
 
1.9%
C 216358
 
1.4%
O 191457
 
1.2%
Other values (9) 145624
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15903627
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 4055994
25.5%
D 3919631
24.6%
E 2314298
14.6%
R 2119919
13.3%
A 1978264
12.4%
N 337495
 
2.1%
I 320207
 
2.0%
T 304380
 
1.9%
C 216358
 
1.4%
O 191457
 
1.2%
Other values (9) 145624
 
0.9%

City
Text

MISSING 

Distinct180
Distinct (%)< 0.1%
Missing119922
Missing (%)5.6%
Memory size16.5 MiB
2023-11-08T23:11:19.189944image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length22
Median length8
Mean length8.1729683
Min length2

Characters and Unicode

Total characters16642697
Distinct characters56
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique105 ?
Unique (%)< 0.1%

Sample

1st rowBROOKLYN
2nd rowBROOKLYN
3rd rowNEW YORK
4th rowQUEENS
5th rowBRONX
ValueCountFrequency (%)
brooklyn 638131
22.8%
new 411014
14.7%
york 410573
14.7%
bronx 387336
13.9%
island 103251
 
3.7%
staten 84322
 
3.0%
jamaica 51129
 
1.8%
astoria 37460
 
1.3%
park 34631
 
1.2%
queens 32410
 
1.2%
Other values (168) 603227
21.6%
2023-11-08T23:11:19.382352image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 2501348
15.0%
N 1902608
11.4%
R 1731548
10.4%
K 1126019
 
6.8%
Y 1099478
 
6.6%
B 1056666
 
6.3%
L 996022
 
6.0%
E 910922
 
5.5%
757174
 
4.5%
A 750632
 
4.5%
Other values (46) 3810280
22.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 15884142
95.4%
Space Separator 757174
 
4.5%
Lowercase Letter 1375
 
< 0.1%
Other Punctuation 6
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 2501348
15.7%
N 1902608
12.0%
R 1731548
10.9%
K 1126019
 
7.1%
Y 1099478
 
6.9%
B 1056666
 
6.7%
L 996022
 
6.3%
E 910922
 
5.7%
A 750632
 
4.7%
S 555863
 
3.5%
Other values (16) 3253036
20.5%
Lowercase Letter
ValueCountFrequency (%)
n 154
11.2%
e 142
10.3%
a 134
9.7%
o 133
9.7%
r 106
 
7.7%
t 93
 
6.8%
l 90
 
6.5%
s 88
 
6.4%
i 67
 
4.9%
u 49
 
3.6%
Other values (16) 319
23.2%
Other Punctuation
ValueCountFrequency (%)
. 4
66.7%
@ 1
 
16.7%
, 1
 
16.7%
Space Separator
ValueCountFrequency (%)
757174
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15885517
95.5%
Common 757180
 
4.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 2501348
15.7%
N 1902608
12.0%
R 1731548
10.9%
K 1126019
 
7.1%
Y 1099478
 
6.9%
B 1056666
 
6.7%
L 996022
 
6.3%
E 910922
 
5.7%
A 750632
 
4.7%
S 555863
 
3.5%
Other values (42) 3254411
20.5%
Common
ValueCountFrequency (%)
757174
> 99.9%
. 4
 
< 0.1%
@ 1
 
< 0.1%
, 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16642697
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 2501348
15.0%
N 1902608
11.4%
R 1731548
10.4%
K 1126019
 
6.8%
Y 1099478
 
6.6%
B 1056666
 
6.3%
L 996022
 
6.0%
E 910922
 
5.5%
757174
 
4.5%
A 750632
 
4.5%
Other values (46) 3810280
22.9%

Landmark
Text

MISSING 

Distinct8558
Distinct (%)0.6%
Missing797021
Missing (%)37.0%
Memory size16.5 MiB
2023-11-08T23:11:19.587723image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length32
Median length29
Mean length13.295101
Min length6

Characters and Unicode

Total characters18070848
Distinct characters43
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique982 ?
Unique (%)0.1%

Sample

1st rowEAST 230 STREET
2nd rowBAY 50 STREET
3rd rowEAST 29 STREET
4th rowWEST 136 STREET
5th rowSHERIDAN AVENUE
ValueCountFrequency (%)
street 614817
20.1%
avenue 500291
 
16.4%
east 125020
 
4.1%
west 119083
 
3.9%
boulevard 48071
 
1.6%
road 44809
 
1.5%
place 44343
 
1.5%
park 21807
 
0.7%
broadway 20333
 
0.7%
parkway 15318
 
0.5%
Other values (4914) 1501798
49.1%
2023-11-08T23:11:19.855738image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 3173073
17.6%
2111863
11.7%
T 1836318
10.2%
A 1338445
 
7.4%
R 1270032
 
7.0%
S 1213497
 
6.7%
N 1024492
 
5.7%
U 701265
 
3.9%
V 617959
 
3.4%
O 581272
 
3.2%
Other values (33) 4202632
23.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 14672867
81.2%
Space Separator 2111863
 
11.7%
Decimal Number 1285741
 
7.1%
Other Punctuation 270
 
< 0.1%
Dash Punctuation 103
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 3173073
21.6%
T 1836318
12.5%
A 1338445
9.1%
R 1270032
8.7%
S 1213497
 
8.3%
N 1024492
 
7.0%
U 701265
 
4.8%
V 617959
 
4.2%
O 581272
 
4.0%
L 411194
 
2.8%
Other values (16) 2505320
17.1%
Decimal Number
ValueCountFrequency (%)
1 293816
22.9%
2 155272
12.1%
3 131223
10.2%
4 115902
 
9.0%
7 113619
 
8.8%
5 113503
 
8.8%
6 98485
 
7.7%
8 96532
 
7.5%
9 89336
 
6.9%
0 78053
 
6.1%
Other Punctuation
ValueCountFrequency (%)
' 136
50.4%
& 125
46.3%
/ 9
 
3.3%
Space Separator
ValueCountFrequency (%)
2111863
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 103
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14672867
81.2%
Common 3397981
 
18.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 3173073
21.6%
T 1836318
12.5%
A 1338445
9.1%
R 1270032
8.7%
S 1213497
 
8.3%
N 1024492
 
7.0%
U 701265
 
4.8%
V 617959
 
4.2%
O 581272
 
4.0%
L 411194
 
2.8%
Other values (16) 2505320
17.1%
Common
ValueCountFrequency (%)
2111863
62.2%
1 293816
 
8.6%
2 155272
 
4.6%
3 131223
 
3.9%
4 115902
 
3.4%
7 113619
 
3.3%
5 113503
 
3.3%
6 98485
 
2.9%
8 96532
 
2.8%
9 89336
 
2.6%
Other values (7) 78430
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18070848
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 3173073
17.6%
2111863
11.7%
T 1836318
10.2%
A 1338445
 
7.4%
R 1270032
 
7.0%
S 1213497
 
6.7%
N 1024492
 
5.7%
U 701265
 
3.9%
V 617959
 
3.4%
O 581272
 
3.2%
Other values (33) 4202632
23.3%

Facility Type
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing2018905
Missing (%)93.6%
Memory size16.5 MiB
N/A
126160 
DSNY Garage
 
11167

Length

Max length11
Median length3
Mean length3.6505349
Min length3

Characters and Unicode

Total characters501317
Distinct characters12
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDSNY Garage
2nd rowN/A
3rd rowN/A
4th rowN/A
5th rowN/A

Common Values

ValueCountFrequency (%)
N/A 126160
 
5.9%
DSNY Garage 11167
 
0.5%
(Missing) 2018905
93.6%

Length

2023-11-08T23:11:19.922527image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-08T23:11:19.971811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
n/a 126160
85.0%
dsny 11167
 
7.5%
garage 11167
 
7.5%

Most occurring characters

ValueCountFrequency (%)
N 137327
27.4%
/ 126160
25.2%
A 126160
25.2%
a 22334
 
4.5%
D 11167
 
2.2%
S 11167
 
2.2%
Y 11167
 
2.2%
11167
 
2.2%
G 11167
 
2.2%
r 11167
 
2.2%
Other values (2) 22334
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 308155
61.5%
Other Punctuation 126160
25.2%
Lowercase Letter 55835
 
11.1%
Space Separator 11167
 
2.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 137327
44.6%
A 126160
40.9%
D 11167
 
3.6%
S 11167
 
3.6%
Y 11167
 
3.6%
G 11167
 
3.6%
Lowercase Letter
ValueCountFrequency (%)
a 22334
40.0%
r 11167
20.0%
g 11167
20.0%
e 11167
20.0%
Other Punctuation
ValueCountFrequency (%)
/ 126160
100.0%
Space Separator
ValueCountFrequency (%)
11167
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 363990
72.6%
Common 137327
 
27.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 137327
37.7%
A 126160
34.7%
a 22334
 
6.1%
D 11167
 
3.1%
S 11167
 
3.1%
Y 11167
 
3.1%
G 11167
 
3.1%
r 11167
 
3.1%
g 11167
 
3.1%
e 11167
 
3.1%
Common
ValueCountFrequency (%)
/ 126160
91.9%
11167
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 501317
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 137327
27.4%
/ 126160
25.2%
A 126160
25.2%
a 22334
 
4.5%
D 11167
 
2.2%
S 11167
 
2.2%
Y 11167
 
2.2%
11167
 
2.2%
G 11167
 
2.2%
r 11167
 
2.2%
Other values (2) 22334
 
4.5%

Status
Categorical

IMBALANCE 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.5 MiB
Closed
1990654 
In Progress
 
98976
Open
 
52491
Assigned
 
9036
Pending
 
2574
Other values (2)
 
2501

Length

Max length11
Median length6
Mean length6.193783
Min length4

Characters and Unicode

Total characters13355233
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOpen
2nd rowOpen
3rd rowOpen
4th rowOpen
5th rowIn Progress

Common Values

ValueCountFrequency (%)
Closed 1990654
92.3%
In Progress 98976
 
4.6%
Open 52491
 
2.4%
Assigned 9036
 
0.4%
Pending 2574
 
0.1%
Started 1302
 
0.1%
Unspecified 1199
 
0.1%

Length

2023-11-08T23:11:20.011729image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-08T23:11:20.067432image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
closed 1990654
88.3%
in 98976
 
4.4%
progress 98976
 
4.4%
open 52491
 
2.3%
assigned 9036
 
0.4%
pending 2574
 
0.1%
started 1302
 
0.1%
unspecified 1199
 
0.1%

Most occurring characters

ValueCountFrequency (%)
s 2207877
16.5%
e 2157431
16.2%
o 2089630
15.6%
d 2004765
15.0%
C 1990654
14.9%
l 1990654
14.9%
r 199254
 
1.5%
n 166850
 
1.2%
g 110586
 
0.8%
P 101550
 
0.8%
Other values (12) 335982
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11001049
82.4%
Uppercase Letter 2255208
 
16.9%
Space Separator 98976
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 2207877
20.1%
e 2157431
19.6%
o 2089630
19.0%
d 2004765
18.2%
l 1990654
18.1%
r 199254
 
1.8%
n 166850
 
1.5%
g 110586
 
1.0%
p 53690
 
0.5%
i 14008
 
0.1%
Other values (4) 6304
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
C 1990654
88.3%
P 101550
 
4.5%
I 98976
 
4.4%
O 52491
 
2.3%
A 9036
 
0.4%
S 1302
 
0.1%
U 1199
 
0.1%
Space Separator
ValueCountFrequency (%)
98976
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13256257
99.3%
Common 98976
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 2207877
16.7%
e 2157431
16.3%
o 2089630
15.8%
d 2004765
15.1%
C 1990654
15.0%
l 1990654
15.0%
r 199254
 
1.5%
n 166850
 
1.3%
g 110586
 
0.8%
P 101550
 
0.8%
Other values (11) 237006
 
1.8%
Common
ValueCountFrequency (%)
98976
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13355233
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 2207877
16.5%
e 2157431
16.2%
o 2089630
15.6%
d 2004765
15.0%
C 1990654
14.9%
l 1990654
14.9%
r 199254
 
1.5%
n 166850
 
1.2%
g 110586
 
0.8%
P 101550
 
0.8%
Other values (12) 335982
 
2.5%

Due Date
Date

MISSING 

Distinct5925
Distinct (%)73.1%
Missing2148125
Missing (%)99.6%
Memory size16.5 MiB
Minimum2023-03-21 09:12:47
Maximum2024-01-24 02:22:23
2023-11-08T23:11:20.120751image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:11:20.179131image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct739
Distinct (%)< 0.1%
Missing55803
Missing (%)2.6%
Memory size16.5 MiB
2023-11-08T23:11:20.397551image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length930
Median length575
Mean length151.18653
Min length3

Characters and Unicode

Total characters317556582
Distinct characters81
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique72 ?
Unique (%)< 0.1%

Sample

1st rowIf the abandoned vehicle meets the criteria to be classified as a derelict (i.e. junk) the Department of Sanitation (DSNY) will investigate and tag the vehicle within three business days.
2nd rowIf the abandoned vehicle meets the criteria to be classified as a derelict (i.e. junk) the Department of Sanitation (DSNY) will investigate and tag the vehicle within three business days.
3rd rowIf the abandoned vehicle meets the criteria to be classified as a derelict (i.e. junk) the Department of Sanitation (DSNY) will investigate and tag the vehicle within three business days.
4th rowThe Department of Transportation referred this complaint to the appropriate Maintenance Unit for repair.
5th rowThe Department of Homeless Services has sent a mobile outreach response team to the location.
ValueCountFrequency (%)
the 6080023
 
12.6%
to 1914555
 
4.0%
department 1847205
 
3.8%
and 1813274
 
3.7%
complaint 1708232
 
3.5%
of 1574285
 
3.3%
police 1113417
 
2.3%
a 853708
 
1.8%
responded 829288
 
1.7%
condition 799290
 
1.7%
Other values (1438) 29876348
61.7%
2023-11-08T23:11:20.687716image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
46457318
14.6%
e 32633328
 
10.3%
t 27579074
 
8.7%
o 24713650
 
7.8%
n 21608051
 
6.8%
i 21200334
 
6.7%
a 18432903
 
5.8%
r 13072333
 
4.1%
s 12323006
 
3.9%
d 10423607
 
3.3%
Other values (71) 89112978
28.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 251776115
79.3%
Space Separator 46516312
 
14.6%
Uppercase Letter 12078569
 
3.8%
Other Punctuation 4856274
 
1.5%
Decimal Number 1316524
 
0.4%
Dash Punctuation 301582
 
0.1%
Control 266146
 
0.1%
Close Punctuation 198068
 
0.1%
Open Punctuation 198068
 
0.1%
Connector Punctuation 48924
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 32633328
13.0%
t 27579074
11.0%
o 24713650
9.8%
n 21608051
 
8.6%
i 21200334
 
8.4%
a 18432903
 
7.3%
r 13072333
 
5.2%
s 12323006
 
4.9%
d 10423607
 
4.1%
l 10256341
 
4.1%
Other values (17) 59533488
23.6%
Uppercase Letter
ValueCountFrequency (%)
T 2570812
21.3%
D 2466716
20.4%
P 1859224
15.4%
N 758773
 
6.3%
C 703609
 
5.8%
Y 594723
 
4.9%
H 579893
 
4.8%
S 424763
 
3.5%
I 375312
 
3.1%
E 266671
 
2.2%
Other values (15) 1478073
12.2%
Decimal Number
ValueCountFrequency (%)
1 397571
30.2%
2 243750
18.5%
3 208471
15.8%
9 129719
 
9.9%
6 126849
 
9.6%
5 80757
 
6.1%
7 69910
 
5.3%
0 38921
 
3.0%
8 11130
 
0.8%
4 9446
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 3734744
76.9%
/ 454528
 
9.4%
, 454314
 
9.4%
' 109239
 
2.2%
: 52234
 
1.1%
" 47707
 
1.0%
; 1718
 
< 0.1%
& 1441
 
< 0.1%
@ 349
 
< 0.1%
Control
ValueCountFrequency (%)
€ 133073
50.0%
™ 54691
20.5%
 39191
 
14.7%
œ 39191
 
14.7%
Space Separator
ValueCountFrequency (%)
46457318
99.9%
  58994
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 301582
100.0%
Close Punctuation
ValueCountFrequency (%)
) 198068
100.0%
Open Punctuation
ValueCountFrequency (%)
( 198068
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 48924
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 263854684
83.1%
Common 53701898
 
16.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 32633328
12.4%
t 27579074
 
10.5%
o 24713650
 
9.4%
n 21608051
 
8.2%
i 21200334
 
8.0%
a 18432903
 
7.0%
r 13072333
 
5.0%
s 12323006
 
4.7%
d 10423607
 
4.0%
l 10256341
 
3.9%
Other values (42) 71612057
27.1%
Common
ValueCountFrequency (%)
46457318
86.5%
. 3734744
 
7.0%
/ 454528
 
0.8%
, 454314
 
0.8%
1 397571
 
0.7%
- 301582
 
0.6%
2 243750
 
0.5%
3 208471
 
0.4%
) 198068
 
0.4%
( 198068
 
0.4%
Other values (19) 1053484
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 317039375
99.8%
None 517207
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
46457318
14.7%
e 32633328
 
10.3%
t 27579074
 
8.7%
o 24713650
 
7.8%
n 21608051
 
6.8%
i 21200334
 
6.7%
a 18432903
 
5.8%
r 13072333
 
4.1%
s 12323006
 
3.9%
d 10423607
 
3.3%
Other values (64) 88595771
27.9%
None
ValueCountFrequency (%)
€ 133073
25.7%
â 133073
25.7%
  58994
11.4%
 58994
11.4%
™ 54691
10.6%
 39191
 
7.6%
œ 39191
 
7.6%
Distinct1321541
Distinct (%)62.8%
Missing51376
Missing (%)2.4%
Memory size16.5 MiB
Minimum2022-08-22 11:44:18
Maximum2023-11-08 22:00:00
2023-11-08T23:11:20.760773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:11:20.818754image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct77
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.5 MiB
2023-11-08T23:11:20.916494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length25
Median length21
Mean length10.489153
Min length8

Characters and Unicode

Total characters22617048
Distinct characters37
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row16 BROOKLYN
2nd row15 BROOKLYN
3rd row12 MANHATTAN
4th row01 QUEENS
5th rowUnspecified BROOKLYN
ValueCountFrequency (%)
brooklyn 667183
15.2%
queens 533769
12.1%
manhattan 456653
 
10.4%
bronx 405022
 
9.2%
12 194052
 
4.4%
05 189609
 
4.3%
01 185523
 
4.2%
03 177685
 
4.0%
07 171715
 
3.9%
04 153259
 
3.5%
Other values (28) 1266674
28.8%
2023-11-08T23:11:21.065106image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 2696640
 
11.9%
2244912
 
9.9%
O 1739388
 
7.7%
0 1552738
 
6.9%
A 1547319
 
6.8%
E 1156218
 
5.1%
T 1090666
 
4.8%
B 1072205
 
4.7%
R 1072205
 
4.7%
1 1008760
 
4.5%
Other values (27) 7435997
32.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 15774283
69.7%
Decimal Number 4247273
 
18.8%
Space Separator 2244912
 
9.9%
Lowercase Letter 350580
 
1.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 2696640
17.1%
O 1739388
11.0%
A 1547319
9.8%
E 1156218
 
7.3%
T 1090666
 
6.9%
B 1072205
 
6.8%
R 1072205
 
6.8%
L 755863
 
4.8%
S 711129
 
4.5%
K 667183
 
4.2%
Other values (8) 3265467
20.7%
Decimal Number
ValueCountFrequency (%)
0 1552738
36.6%
1 1008760
23.8%
2 344944
 
8.1%
3 236534
 
5.6%
5 228309
 
5.4%
4 223379
 
5.3%
7 210078
 
4.9%
8 175231
 
4.1%
9 134300
 
3.2%
6 133000
 
3.1%
Lowercase Letter
ValueCountFrequency (%)
e 70116
20.0%
i 70116
20.0%
n 35058
10.0%
s 35058
10.0%
p 35058
10.0%
c 35058
10.0%
f 35058
10.0%
d 35058
10.0%
Space Separator
ValueCountFrequency (%)
2244912
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16124863
71.3%
Common 6492185
28.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 2696640
16.7%
O 1739388
10.8%
A 1547319
9.6%
E 1156218
 
7.2%
T 1090666
 
6.8%
B 1072205
 
6.6%
R 1072205
 
6.6%
L 755863
 
4.7%
S 711129
 
4.4%
K 667183
 
4.1%
Other values (16) 3616047
22.4%
Common
ValueCountFrequency (%)
2244912
34.6%
0 1552738
23.9%
1 1008760
15.5%
2 344944
 
5.3%
3 236534
 
3.6%
5 228309
 
3.5%
4 223379
 
3.4%
7 210078
 
3.2%
8 175231
 
2.7%
9 134300
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22617048
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 2696640
 
11.9%
2244912
 
9.9%
O 1739388
 
7.7%
0 1552738
 
6.9%
A 1547319
 
6.8%
E 1156218
 
5.1%
T 1090666
 
4.8%
B 1072205
 
4.7%
R 1072205
 
4.7%
1 1008760
 
4.5%
Other values (27) 7435997
32.9%

BBL
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct339263
Distinct (%)17.9%
Missing261151
Missing (%)12.1%
Infinite0
Infinite (%)0.0%
Mean2.7445135 × 109
Minimum0
Maximum5.2700005 × 109
Zeros367
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size16.5 MiB
2023-11-08T23:11:21.130692image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.0079 × 109
Q12.02744 × 109
median3.01971 × 109
Q34.0089301 × 109
95-th percentile4.15572 × 109
Maximum5.2700005 × 109
Range5.2700005 × 109
Interquartile range (IQR)1.98149 × 109

Descriptive statistics

Standard deviation1.1775907 × 109
Coefficient of variation (CV)0.42907083
Kurtosis-1.0316237
Mean2.7445135 × 109
Median Absolute Deviation (MAD)9.9175 × 108
Skewness-0.077409369
Sum5.2010754 × 1015
Variance1.3867199 × 1018
MonotonicityNot monotonic
2023-11-08T23:11:21.187616image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4068290001 8168
 
0.4%
4068580001 3830
 
0.2%
4114340007 3781
 
0.2%
1011110001 3021
 
0.1%
2025110068 2811
 
0.1%
3003370027 2583
 
0.1%
2048330028 2305
 
0.1%
3044520423 2277
 
0.1%
2029020036 2157
 
0.1%
4142600001 2118
 
0.1%
Other values (339253) 1862030
86.4%
(Missing) 261151
 
12.1%
ValueCountFrequency (%)
0 367
< 0.1%
1000010010 12
 
< 0.1%
1000010101 1
 
< 0.1%
1000010111 1
 
< 0.1%
1000010201 2
 
< 0.1%
1000020001 19
 
< 0.1%
1000020002 40
 
< 0.1%
1000020023 7
 
< 0.1%
1000030001 141
 
< 0.1%
1000030010 13
 
< 0.1%
ValueCountFrequency (%)
5270000519 2
 
< 0.1%
5270000511 2
 
< 0.1%
5270000501 9
< 0.1%
5240009999 1
 
< 0.1%
5200479999 1
 
< 0.1%
5200429999 5
< 0.1%
5200399999 1
 
< 0.1%
5200389999 2
 
< 0.1%
5200379999 3
 
< 0.1%
5200169999 1
 
< 0.1%

Borough
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.5 MiB
BROOKLYN
667183 
QUEENS
533762 
MANHATTAN
457593 
BRONX
404089 
STATEN ISLAND
88680 

Length

Max length13
Median length11
Mean length7.3674043
Min length5

Characters and Unicode

Total characters15885833
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBROOKLYN
2nd rowBROOKLYN
3rd rowMANHATTAN
4th rowQUEENS
5th rowBROOKLYN

Common Values

ValueCountFrequency (%)
BROOKLYN 667183
30.9%
QUEENS 533762
24.8%
MANHATTAN 457593
21.2%
BRONX 404089
18.7%
STATEN ISLAND 88680
 
4.1%
Unspecified 4925
 
0.2%

Length

2023-11-08T23:11:21.239861image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-08T23:11:21.297237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
brooklyn 667183
29.7%
queens 533762
23.8%
manhattan 457593
20.4%
bronx 404089
18.0%
staten 88680
 
4.0%
island 88680
 
4.0%
unspecified 4925
 
0.2%

Most occurring characters

ValueCountFrequency (%)
N 2697580
17.0%
O 1738455
10.9%
A 1550139
9.8%
E 1156204
 
7.3%
T 1092546
 
6.9%
B 1071272
 
6.7%
R 1071272
 
6.7%
L 755863
 
4.8%
S 711122
 
4.5%
K 667183
 
4.2%
Other values (17) 3374197
21.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 15747903
99.1%
Space Separator 88680
 
0.6%
Lowercase Letter 49250
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 2697580
17.1%
O 1738455
11.0%
A 1550139
9.8%
E 1156204
 
7.3%
T 1092546
 
6.9%
B 1071272
 
6.8%
R 1071272
 
6.8%
L 755863
 
4.8%
S 711122
 
4.5%
K 667183
 
4.2%
Other values (8) 3236267
20.6%
Lowercase Letter
ValueCountFrequency (%)
e 9850
20.0%
i 9850
20.0%
n 4925
10.0%
s 4925
10.0%
p 4925
10.0%
c 4925
10.0%
f 4925
10.0%
d 4925
10.0%
Space Separator
ValueCountFrequency (%)
88680
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15797153
99.4%
Common 88680
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 2697580
17.1%
O 1738455
11.0%
A 1550139
9.8%
E 1156204
 
7.3%
T 1092546
 
6.9%
B 1071272
 
6.8%
R 1071272
 
6.8%
L 755863
 
4.8%
S 711122
 
4.5%
K 667183
 
4.2%
Other values (16) 3285517
20.8%
Common
ValueCountFrequency (%)
88680
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15885833
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 2697580
17.0%
O 1738455
10.9%
A 1550139
9.8%
E 1156204
 
7.3%
T 1092546
 
6.9%
B 1071272
 
6.7%
R 1071272
 
6.7%
L 755863
 
4.8%
S 711122
 
4.5%
K 667183
 
4.2%
Other values (17) 3374197
21.2%

X Coordinate (State Plane)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct108621
Distinct (%)5.1%
Missing33541
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean1005322.7
Minimum913353
Maximum1067281
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 MiB
2023-11-08T23:11:21.353793image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum913353
5-th percentile978282
Q1992506
median1004135
Q31018124
95-th percentile1042283
Maximum1067281
Range153928
Interquartile range (IQR)25618

Descriptive statistics

Standard deviation21603.603
Coefficient of variation (CV)0.021489223
Kurtosis1.5962722
Mean1005322.7
Median Absolute Deviation (MAD)12660
Skewness-0.32947563
Sum2.1339894 × 1012
Variance4.6671566 × 108
MonotonicityNot monotonic
2023-11-08T23:11:21.409518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1037000 8195
 
0.4%
1038740 3860
 
0.2%
994623 2806
 
0.1%
1003936 2648
 
0.1%
984166 2610
 
0.1%
1022911 2320
 
0.1%
1019422 2296
 
0.1%
1026729 2274
 
0.1%
1000482 2024
 
0.1%
1043001 1973
 
0.1%
Other values (108611) 2091685
97.0%
(Missing) 33541
 
1.6%
ValueCountFrequency (%)
913353 1
 
< 0.1%
913412 1
 
< 0.1%
913414 1
 
< 0.1%
913432 2
< 0.1%
913444 1
 
< 0.1%
913459 3
< 0.1%
913512 1
 
< 0.1%
913554 1
 
< 0.1%
913628 1
 
< 0.1%
913683 1
 
< 0.1%
ValueCountFrequency (%)
1067281 4
< 0.1%
1067279 2
 
< 0.1%
1067220 2
 
< 0.1%
1067180 2
 
< 0.1%
1067178 6
< 0.1%
1067176 2
 
< 0.1%
1067173 2
 
< 0.1%
1067133 1
 
< 0.1%
1067132 1
 
< 0.1%
1067131 2
 
< 0.1%

Y Coordinate (State Plane)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct121969
Distinct (%)5.7%
Missing32919
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean206249.2
Minimum121098
Maximum271876
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 MiB
2023-11-08T23:11:21.467207image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum121098
5-th percentile156949
Q1184165
median203853
Q3233032
95-th percentile255604
Maximum271876
Range150778
Interquartile range (IQR)48867

Descriptive statistics

Standard deviation30846.404
Coefficient of variation (CV)0.1495589
Kurtosis-0.8260381
Mean206249.2
Median Absolute Deviation (MAD)22655
Skewness0.023847716
Sum4.379316 × 1011
Variance9.5150064 × 108
MonotonicityNot monotonic
2023-11-08T23:11:21.522642image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202363 8212
 
0.4%
200651 3879
 
0.2%
222956 2704
 
0.1%
188346 2618
 
0.1%
242233 2608
 
0.1%
264242 2306
 
0.1%
177846 2294
 
0.1%
182858 2291
 
0.1%
237497 1992
 
0.1%
175548 1973
 
0.1%
Other values (121959) 2092436
97.0%
(Missing) 32919
 
1.5%
ValueCountFrequency (%)
121098 1
 
< 0.1%
121140 1
 
< 0.1%
121152 1
 
< 0.1%
121189 2
 
< 0.1%
121213 1
 
< 0.1%
121268 1
 
< 0.1%
121280 3
 
< 0.1%
121305 5
 
< 0.1%
121315 1
 
< 0.1%
121316 15
< 0.1%
ValueCountFrequency (%)
271876 23
< 0.1%
271861 1
 
< 0.1%
271730 4
 
< 0.1%
271676 17
< 0.1%
271672 1
 
< 0.1%
271664 6
 
< 0.1%
271660 3
 
< 0.1%
271640 1
 
< 0.1%
271639 3
 
< 0.1%
271629 1
 
< 0.1%

Open Data Channel Type
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.5 MiB
ONLINE
905705 
PHONE
662826 
MOBILE
419574 
UNKNOWN
167976 
OTHER
 
151

Length

Max length7
Median length6
Mean length5.7704324
Min length5

Characters and Unicode

Total characters12442391
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPHONE
2nd rowPHONE
3rd rowPHONE
4th rowUNKNOWN
5th rowMOBILE

Common Values

ValueCountFrequency (%)
ONLINE 905705
42.0%
PHONE 662826
30.7%
MOBILE 419574
19.5%
UNKNOWN 167976
 
7.8%
OTHER 151
 
< 0.1%

Length

2023-11-08T23:11:21.579337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-08T23:11:21.635351image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
online 905705
42.0%
phone 662826
30.7%
mobile 419574
19.5%
unknown 167976
 
7.8%
other 151
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 2978164
23.9%
O 2156232
17.3%
E 1988256
16.0%
L 1325279
10.7%
I 1325279
10.7%
H 662977
 
5.3%
P 662826
 
5.3%
M 419574
 
3.4%
B 419574
 
3.4%
U 167976
 
1.4%
Other values (4) 336254
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 12442391
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 2978164
23.9%
O 2156232
17.3%
E 1988256
16.0%
L 1325279
10.7%
I 1325279
10.7%
H 662977
 
5.3%
P 662826
 
5.3%
M 419574
 
3.4%
B 419574
 
3.4%
U 167976
 
1.4%
Other values (4) 336254
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 12442391
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 2978164
23.9%
O 2156232
17.3%
E 1988256
16.0%
L 1325279
10.7%
I 1325279
10.7%
H 662977
 
5.3%
P 662826
 
5.3%
M 419574
 
3.4%
B 419574
 
3.4%
U 167976
 
1.4%
Other values (4) 336254
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12442391
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 2978164
23.9%
O 2156232
17.3%
E 1988256
16.0%
L 1325279
10.7%
I 1325279
10.7%
H 662977
 
5.3%
P 662826
 
5.3%
M 419574
 
3.4%
B 419574
 
3.4%
U 167976
 
1.4%
Other values (4) 336254
 
2.7%
Distinct1295
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.5 MiB
2023-11-08T23:11:21.895023image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length57
Median length11
Mean length11.03361
Min length3

Characters and Unicode

Total characters23791022
Distinct characters78
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique460 ?
Unique (%)< 0.1%

Sample

1st rowUnspecified
2nd rowUnspecified
3rd rowUnspecified
4th rowUnspecified
5th rowUnspecified
ValueCountFrequency (%)
unspecified 2141704
98.3%
park 8297
 
0.4%
playground 2304
 
0.1%
n/a 2141
 
0.1%
central 1442
 
0.1%
corona 735
 
< 0.1%
meadows 726
 
< 0.1%
flushing 726
 
< 0.1%
leif 616
 
< 0.1%
ericson 616
 
< 0.1%
Other values (1616) 18774
 
0.9%
2023-11-08T23:11:22.227169image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 4297371
18.1%
i 4291777
18.0%
n 2156365
9.1%
s 2148692
9.0%
d 2148165
9.0%
c 2145076
9.0%
f 2142835
9.0%
p 2142806
9.0%
U 2141960
9.0%
a 23094
 
0.1%
Other values (68) 152881
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 21583291
90.7%
Uppercase Letter 2180442
 
9.2%
Space Separator 21849
 
0.1%
Other Punctuation 3970
 
< 0.1%
Decimal Number 1054
 
< 0.1%
Open Punctuation 161
 
< 0.1%
Close Punctuation 157
 
< 0.1%
Dash Punctuation 89
 
< 0.1%
Control 6
 
< 0.1%
Format 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4297371
19.9%
i 4291777
19.9%
n 2156365
10.0%
s 2148692
10.0%
d 2148165
10.0%
c 2145076
9.9%
f 2142835
9.9%
p 2142806
9.9%
a 23094
 
0.1%
r 22403
 
0.1%
Other values (17) 64707
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
U 2141960
98.2%
P 12032
 
0.6%
C 3777
 
0.2%
A 2864
 
0.1%
S 2470
 
0.1%
N 2415
 
0.1%
M 2121
 
0.1%
F 1484
 
0.1%
B 1458
 
0.1%
H 1346
 
0.1%
Other values (17) 8515
 
0.4%
Decimal Number
ValueCountFrequency (%)
1 226
21.4%
0 178
16.9%
2 122
11.6%
4 94
8.9%
9 93
8.8%
5 82
 
7.8%
6 79
 
7.5%
3 66
 
6.3%
8 62
 
5.9%
7 52
 
4.9%
Other Punctuation
ValueCountFrequency (%)
/ 2203
55.5%
. 1292
32.5%
' 401
 
10.1%
" 30
 
0.8%
& 22
 
0.6%
, 17
 
0.4%
% 5
 
0.1%
Control
ValueCountFrequency (%)
€ 3
50.0%
™ 3
50.0%
Space Separator
ValueCountFrequency (%)
21849
100.0%
Open Punctuation
ValueCountFrequency (%)
( 161
100.0%
Close Punctuation
ValueCountFrequency (%)
) 157
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 89
100.0%
Format
ValueCountFrequency (%)
­ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23763733
99.9%
Common 27289
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4297371
18.1%
i 4291777
18.1%
n 2156365
9.1%
s 2148692
9.0%
d 2148165
9.0%
c 2145076
9.0%
f 2142835
9.0%
p 2142806
9.0%
U 2141960
9.0%
a 23094
 
0.1%
Other values (44) 125592
 
0.5%
Common
ValueCountFrequency (%)
21849
80.1%
/ 2203
 
8.1%
. 1292
 
4.7%
' 401
 
1.5%
1 226
 
0.8%
0 178
 
0.7%
( 161
 
0.6%
) 157
 
0.6%
2 122
 
0.4%
4 94
 
0.3%
Other values (14) 606
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23791007
> 99.9%
None 15
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 4297371
18.1%
i 4291777
18.0%
n 2156365
9.1%
s 2148692
9.0%
d 2148165
9.0%
c 2145076
9.0%
f 2142835
9.0%
p 2142806
9.0%
U 2141960
9.0%
a 23094
 
0.1%
Other values (63) 152866
 
0.6%
None
ValueCountFrequency (%)
à 3
20.0%
­ 3
20.0%
â 3
20.0%
€ 3
20.0%
™ 3
20.0%

Park Borough
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.5 MiB
BROOKLYN
667183 
QUEENS
533762 
MANHATTAN
457593 
BRONX
404089 
STATEN ISLAND
88680 

Length

Max length13
Median length11
Mean length7.3674043
Min length5

Characters and Unicode

Total characters15885833
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBROOKLYN
2nd rowBROOKLYN
3rd rowMANHATTAN
4th rowQUEENS
5th rowBROOKLYN

Common Values

ValueCountFrequency (%)
BROOKLYN 667183
30.9%
QUEENS 533762
24.8%
MANHATTAN 457593
21.2%
BRONX 404089
18.7%
STATEN ISLAND 88680
 
4.1%
Unspecified 4925
 
0.2%

Length

2023-11-08T23:11:22.291162image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-08T23:11:22.347386image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
brooklyn 667183
29.7%
queens 533762
23.8%
manhattan 457593
20.4%
bronx 404089
18.0%
staten 88680
 
4.0%
island 88680
 
4.0%
unspecified 4925
 
0.2%

Most occurring characters

ValueCountFrequency (%)
N 2697580
17.0%
O 1738455
10.9%
A 1550139
9.8%
E 1156204
 
7.3%
T 1092546
 
6.9%
B 1071272
 
6.7%
R 1071272
 
6.7%
L 755863
 
4.8%
S 711122
 
4.5%
K 667183
 
4.2%
Other values (17) 3374197
21.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 15747903
99.1%
Space Separator 88680
 
0.6%
Lowercase Letter 49250
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 2697580
17.1%
O 1738455
11.0%
A 1550139
9.8%
E 1156204
 
7.3%
T 1092546
 
6.9%
B 1071272
 
6.8%
R 1071272
 
6.8%
L 755863
 
4.8%
S 711122
 
4.5%
K 667183
 
4.2%
Other values (8) 3236267
20.6%
Lowercase Letter
ValueCountFrequency (%)
e 9850
20.0%
i 9850
20.0%
n 4925
10.0%
s 4925
10.0%
p 4925
10.0%
c 4925
10.0%
f 4925
10.0%
d 4925
10.0%
Space Separator
ValueCountFrequency (%)
88680
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15797153
99.4%
Common 88680
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 2697580
17.1%
O 1738455
11.0%
A 1550139
9.8%
E 1156204
 
7.3%
T 1092546
 
6.9%
B 1071272
 
6.8%
R 1071272
 
6.8%
L 755863
 
4.8%
S 711122
 
4.5%
K 667183
 
4.2%
Other values (16) 3285517
20.8%
Common
ValueCountFrequency (%)
88680
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15885833
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 2697580
17.0%
O 1738455
10.9%
A 1550139
9.8%
E 1156204
 
7.3%
T 1092546
 
6.9%
B 1071272
 
6.7%
R 1071272
 
6.7%
L 755863
 
4.8%
S 711122
 
4.5%
K 667183
 
4.2%
Other values (17) 3374197
21.2%

Vehicle Type
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct4
Distinct (%)0.8%
Missing2155736
Missing (%)> 99.9%
Memory size16.5 MiB
Car Service
464 
Ambulette / Paratransit
 
16
Commuter Van
 
14
Green Taxi
 
2

Length

Max length23
Median length11
Mean length11.41129
Min length10

Characters and Unicode

Total characters5660
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCar Service
2nd rowCar Service
3rd rowCar Service
4th rowCar Service
5th rowAmbulette / Paratransit

Common Values

ValueCountFrequency (%)
Car Service 464
 
< 0.1%
Ambulette / Paratransit 16
 
< 0.1%
Commuter Van 14
 
< 0.1%
Green Taxi 2
 
< 0.1%
(Missing) 2155736
> 99.9%

Length

2023-11-08T23:11:22.398803image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-08T23:11:22.444635image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
car 464
46.0%
service 464
46.0%
ambulette 16
 
1.6%
16
 
1.6%
paratransit 16
 
1.6%
commuter 14
 
1.4%
van 14
 
1.4%
green 2
 
0.2%
taxi 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e 978
17.3%
r 976
17.2%
a 528
9.3%
512
9.0%
i 482
8.5%
C 478
8.4%
S 464
8.2%
v 464
8.2%
c 464
8.2%
t 78
 
1.4%
Other values (14) 236
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4140
73.1%
Uppercase Letter 992
 
17.5%
Space Separator 512
 
9.0%
Other Punctuation 16
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 978
23.6%
r 976
23.6%
a 528
12.8%
i 482
11.6%
v 464
11.2%
c 464
11.2%
t 78
 
1.9%
m 44
 
1.1%
n 32
 
0.8%
u 30
 
0.7%
Other values (5) 64
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
C 478
48.2%
S 464
46.8%
P 16
 
1.6%
A 16
 
1.6%
V 14
 
1.4%
G 2
 
0.2%
T 2
 
0.2%
Space Separator
ValueCountFrequency (%)
512
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5132
90.7%
Common 528
 
9.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 978
19.1%
r 976
19.0%
a 528
10.3%
i 482
9.4%
C 478
9.3%
S 464
9.0%
v 464
9.0%
c 464
9.0%
t 78
 
1.5%
m 44
 
0.9%
Other values (12) 176
 
3.4%
Common
ValueCountFrequency (%)
512
97.0%
/ 16
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5660
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 978
17.3%
r 976
17.2%
a 528
9.3%
512
9.0%
i 482
8.5%
C 478
8.4%
S 464
8.2%
v 464
8.2%
c 464
8.2%
t 78
 
1.4%
Other values (14) 236
 
4.2%

Taxi Company Borough
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)0.4%
Missing2155022
Missing (%)99.9%
Memory size16.5 MiB
MANHATTAN
390 
QUEENS
295 
BROOKLYN
288 
BRONX
208 
STATEN ISLAND
 
29

Length

Max length13
Median length9
Mean length7.438843
Min length5

Characters and Unicode

Total characters9001
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMANHATTAN
2nd rowQUEENS
3rd rowQUEENS
4th rowQUEENS
5th rowBROOKLYN

Common Values

ValueCountFrequency (%)
MANHATTAN 390
 
< 0.1%
QUEENS 295
 
< 0.1%
BROOKLYN 288
 
< 0.1%
BRONX 208
 
< 0.1%
STATEN ISLAND 29
 
< 0.1%
(Missing) 2155022
99.9%

Length

2023-11-08T23:11:22.488137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-08T23:11:22.540250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
manhattan 390
31.5%
queens 295
23.8%
brooklyn 288
23.2%
bronx 208
16.8%
staten 29
 
2.3%
island 29
 
2.3%

Most occurring characters

ValueCountFrequency (%)
N 1629
18.1%
A 1228
13.6%
T 838
9.3%
O 784
8.7%
E 619
 
6.9%
B 496
 
5.5%
R 496
 
5.5%
M 390
 
4.3%
H 390
 
4.3%
S 353
 
3.9%
Other values (9) 1778
19.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 8972
99.7%
Space Separator 29
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 1629
18.2%
A 1228
13.7%
T 838
9.3%
O 784
8.7%
E 619
 
6.9%
B 496
 
5.5%
R 496
 
5.5%
M 390
 
4.3%
H 390
 
4.3%
S 353
 
3.9%
Other values (8) 1749
19.5%
Space Separator
ValueCountFrequency (%)
29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8972
99.7%
Common 29
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 1629
18.2%
A 1228
13.7%
T 838
9.3%
O 784
8.7%
E 619
 
6.9%
B 496
 
5.5%
R 496
 
5.5%
M 390
 
4.3%
H 390
 
4.3%
S 353
 
3.9%
Other values (8) 1749
19.5%
Common
ValueCountFrequency (%)
29
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 1629
18.1%
A 1228
13.6%
T 838
9.3%
O 784
8.7%
E 619
 
6.9%
B 496
 
5.5%
R 496
 
5.5%
M 390
 
4.3%
H 390
 
4.3%
S 353
 
3.9%
Other values (9) 1778
19.8%

Taxi Pick Up Location
Text

MISSING 

Distinct12614
Distinct (%)53.0%
Missing2132427
Missing (%)98.9%
Memory size16.5 MiB
2023-11-08T23:11:22.740686image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length60
Median length57
Mean length48.655325
Min length11

Characters and Unicode

Total characters1158240
Distinct characters65
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9196 ?
Unique (%)38.6%

Sample

1st row125 COLUMBUS AVENUE, MANHATTAN (NEW YORK), NY, 10023
2nd row215 EAST 68 STREET, MANHATTAN (NEW YORK), NY, 10065
3rd row641 8 AVENUE, MANHATTAN (NEW YORK), NY, 10036
4th row37 BLUE SLIP, BROOKLYN, NY, 11222
5th row315 WEST 20 STREET, MANHATTAN (NEW YORK), NY, 10011
ValueCountFrequency (%)
ny 23670
 
12.5%
manhattan 15126
 
8.0%
york 12461
 
6.6%
new 12362
 
6.5%
street 11085
 
5.8%
avenue 9547
 
5.0%
east 4964
 
2.6%
queens 4489
 
2.4%
west 3753
 
2.0%
brooklyn 3490
 
1.8%
Other values (4057) 88917
46.8%
2023-11-08T23:11:23.012491image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
179912
15.5%
N 101842
 
8.8%
A 86904
 
7.5%
E 85518
 
7.4%
T 71981
 
6.2%
, 71080
 
6.1%
1 56576
 
4.9%
0 45470
 
3.9%
R 44589
 
3.8%
Y 44373
 
3.8%
Other values (55) 369995
31.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 681662
58.9%
Decimal Number 190259
 
16.4%
Space Separator 179912
 
15.5%
Other Punctuation 71085
 
6.1%
Open Punctuation 16604
 
1.4%
Close Punctuation 16602
 
1.4%
Dash Punctuation 1266
 
0.1%
Lowercase Letter 850
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 101842
14.9%
A 86904
12.7%
E 85518
12.5%
T 71981
10.6%
R 44589
 
6.5%
Y 44373
 
6.5%
O 33625
 
4.9%
S 31566
 
4.6%
H 21313
 
3.1%
M 20787
 
3.0%
Other values (16) 139164
20.4%
Lowercase Letter
ValueCountFrequency (%)
r 191
22.5%
i 120
14.1%
t 116
13.6%
o 88
10.4%
p 80
9.4%
a 77
9.1%
e 39
 
4.6%
d 33
 
3.9%
u 23
 
2.7%
n 20
 
2.4%
Other values (11) 63
 
7.4%
Decimal Number
ValueCountFrequency (%)
1 56576
29.7%
0 45470
23.9%
2 19282
 
10.1%
3 16213
 
8.5%
4 11117
 
5.8%
5 9900
 
5.2%
6 9644
 
5.1%
7 7783
 
4.1%
9 7307
 
3.8%
8 6967
 
3.7%
Other Punctuation
ValueCountFrequency (%)
, 71080
> 99.9%
/ 2
 
< 0.1%
' 2
 
< 0.1%
& 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
179912
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16604
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16602
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1266
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 682512
58.9%
Common 475728
41.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 101842
14.9%
A 86904
12.7%
E 85518
12.5%
T 71981
10.5%
R 44589
 
6.5%
Y 44373
 
6.5%
O 33625
 
4.9%
S 31566
 
4.6%
H 21313
 
3.1%
M 20787
 
3.0%
Other values (37) 140014
20.5%
Common
ValueCountFrequency (%)
179912
37.8%
, 71080
 
14.9%
1 56576
 
11.9%
0 45470
 
9.6%
2 19282
 
4.1%
( 16604
 
3.5%
) 16602
 
3.5%
3 16213
 
3.4%
4 11117
 
2.3%
5 9900
 
2.1%
Other values (8) 32972
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1158240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
179912
15.5%
N 101842
 
8.8%
A 86904
 
7.5%
E 85518
 
7.4%
T 71981
 
6.2%
, 71080
 
6.1%
1 56576
 
4.9%
0 45470
 
3.9%
R 44589
 
3.8%
Y 44373
 
3.8%
Other values (55) 369995
31.9%

Bridge Highway Name
Text

MISSING 

Distinct77
Distinct (%)0.5%
Missing2140247
Missing (%)99.3%
Memory size16.5 MiB
2023-11-08T23:11:23.181837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length42
Median length1
Mean length4.2508602
Min length1

Characters and Unicode

Total characters67950
Distinct characters62
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row4
2nd rowE
3rd row7
4th row6
5th row2
ValueCountFrequency (%)
1 1586
 
7.2%
r 1547
 
7.0%
expwy 1497
 
6.8%
f 1062
 
4.8%
pkwy 1010
 
4.6%
6 981
 
4.5%
e 933
 
4.2%
2 928
 
4.2%
a 772
 
3.5%
q 642
 
2.9%
Other values (112) 11021
50.1%
2023-11-08T23:11:23.401875image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5994
 
8.8%
n 3667
 
5.4%
r 3216
 
4.7%
y 3176
 
4.7%
w 3170
 
4.7%
E 2794
 
4.1%
a 2732
 
4.0%
e 2657
 
3.9%
R 2404
 
3.5%
o 2219
 
3.3%
Other values (52) 35921
52.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 36641
53.9%
Uppercase Letter 19189
28.2%
Space Separator 5994
 
8.8%
Decimal Number 5188
 
7.6%
Other Punctuation 843
 
1.2%
Dash Punctuation 95
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 3667
 
10.0%
r 3216
 
8.8%
y 3176
 
8.7%
w 3170
 
8.7%
a 2732
 
7.5%
e 2657
 
7.3%
o 2219
 
6.1%
t 2085
 
5.7%
s 2049
 
5.6%
k 1758
 
4.8%
Other values (14) 9912
27.1%
Uppercase Letter
ValueCountFrequency (%)
E 2794
14.6%
R 2404
12.5%
B 1540
 
8.0%
F 1427
 
7.4%
P 1305
 
6.8%
D 1269
 
6.6%
C 1158
 
6.0%
Q 1081
 
5.6%
A 987
 
5.1%
G 959
 
5.0%
Other values (13) 4265
22.2%
Decimal Number
ValueCountFrequency (%)
1 1620
31.2%
6 981
18.9%
2 973
18.8%
4 614
 
11.8%
3 296
 
5.7%
7 261
 
5.0%
9 235
 
4.5%
5 198
 
3.8%
8 7
 
0.1%
0 3
 
0.1%
Other Punctuation
ValueCountFrequency (%)
/ 766
90.9%
. 59
 
7.0%
, 18
 
2.1%
Space Separator
ValueCountFrequency (%)
5994
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 95
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 55830
82.2%
Common 12120
 
17.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 3667
 
6.6%
r 3216
 
5.8%
y 3176
 
5.7%
w 3170
 
5.7%
E 2794
 
5.0%
a 2732
 
4.9%
e 2657
 
4.8%
R 2404
 
4.3%
o 2219
 
4.0%
t 2085
 
3.7%
Other values (37) 27710
49.6%
Common
ValueCountFrequency (%)
5994
49.5%
1 1620
 
13.4%
6 981
 
8.1%
2 973
 
8.0%
/ 766
 
6.3%
4 614
 
5.1%
3 296
 
2.4%
7 261
 
2.2%
9 235
 
1.9%
5 198
 
1.6%
Other values (5) 182
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67950
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5994
 
8.8%
n 3667
 
5.4%
r 3216
 
4.7%
y 3176
 
4.7%
w 3170
 
4.7%
E 2794
 
4.1%
a 2732
 
4.0%
e 2657
 
3.9%
R 2404
 
3.5%
o 2219
 
3.3%
Other values (52) 35921
52.9%
Distinct244
Distinct (%)2.9%
Missing2147873
Missing (%)99.6%
Memory size16.5 MiB
2023-11-08T23:11:23.567126image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length54
Median length36
Mean length19.154684
Min length8

Characters and Unicode

Total characters160114
Distinct characters64
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)0.3%

Sample

1st row6 Uptown & The Bronx
2nd row2 3 Downtown & Brooklyn
3rd rowL to Brooklyn
4th rowB D F M Uptown & The Bronx - Queens
5th rowM R to Manhattan
ValueCountFrequency (%)
2769
 
9.1%
bound 2206
 
7.2%
downtown 1592
 
5.2%
to 1481
 
4.9%
uptown 1440
 
4.7%
brooklyn 1167
 
3.8%
bronx 1093
 
3.6%
the 1078
 
3.5%
1 1067
 
3.5%
r 904
 
3.0%
Other values (145) 15724
51.5%
2023-11-08T23:11:23.789337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22162
 
13.8%
o 18323
 
11.4%
n 15578
 
9.7%
t 11715
 
7.3%
r 6227
 
3.9%
B 6123
 
3.8%
a 5925
 
3.7%
w 5762
 
3.6%
e 5570
 
3.5%
u 5377
 
3.4%
Other values (54) 57352
35.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 103045
64.4%
Uppercase Letter 25296
 
15.8%
Space Separator 22162
 
13.8%
Other Punctuation 5131
 
3.2%
Decimal Number 3816
 
2.4%
Dash Punctuation 474
 
0.3%
Open Punctuation 95
 
0.1%
Close Punctuation 95
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 18323
17.8%
n 15578
15.1%
t 11715
11.4%
r 6227
 
6.0%
a 5925
 
5.7%
w 5762
 
5.6%
e 5570
 
5.4%
u 5377
 
5.2%
d 4412
 
4.3%
h 4218
 
4.1%
Other values (14) 19938
19.3%
Uppercase Letter
ValueCountFrequency (%)
B 6123
24.2%
D 2103
 
8.3%
T 1765
 
7.0%
U 1608
 
6.4%
W 1455
 
5.8%
E 1398
 
5.5%
M 1238
 
4.9%
R 1207
 
4.8%
N 1200
 
4.7%
S 1131
 
4.5%
Other values (14) 6068
24.0%
Decimal Number
ValueCountFrequency (%)
1 1083
28.4%
2 608
15.9%
6 562
14.7%
3 558
14.6%
5 447
11.7%
4 358
 
9.4%
7 76
 
2.0%
9 51
 
1.3%
8 47
 
1.2%
0 26
 
0.7%
Other Punctuation
ValueCountFrequency (%)
/ 2832
55.2%
& 2299
44.8%
Space Separator
ValueCountFrequency (%)
22162
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 474
100.0%
Open Punctuation
ValueCountFrequency (%)
( 95
100.0%
Close Punctuation
ValueCountFrequency (%)
) 95
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 128341
80.2%
Common 31773
 
19.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 18323
14.3%
n 15578
 
12.1%
t 11715
 
9.1%
r 6227
 
4.9%
B 6123
 
4.8%
a 5925
 
4.6%
w 5762
 
4.5%
e 5570
 
4.3%
u 5377
 
4.2%
d 4412
 
3.4%
Other values (38) 43329
33.8%
Common
ValueCountFrequency (%)
22162
69.8%
/ 2832
 
8.9%
& 2299
 
7.2%
1 1083
 
3.4%
2 608
 
1.9%
6 562
 
1.8%
3 558
 
1.8%
- 474
 
1.5%
5 447
 
1.4%
4 358
 
1.1%
Other values (6) 390
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 160114
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22162
 
13.8%
o 18323
 
11.4%
n 15578
 
9.7%
t 11715
 
7.3%
r 6227
 
3.9%
B 6123
 
3.8%
a 5925
 
3.7%
w 5762
 
3.6%
e 5570
 
3.5%
u 5377
 
3.4%
Other values (54) 57352
35.8%

Road Ramp
Text

MISSING 

Distinct410
Distinct (%)8.5%
Missing2151397
Missing (%)99.8%
Memory size16.5 MiB
2023-11-08T23:11:24.026637image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length40
Median length39
Mean length11.671768
Min length3

Characters and Unicode

Total characters56433
Distinct characters67
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique177 ?
Unique (%)3.7%

Sample

1st rowMontrose Av & Bushwick Av
2nd rowRoadway
3rd rowRoadway
4th rowRoadway
5th rowRoadway
ValueCountFrequency (%)
roadway 2204
16.9%
1582
 
12.2%
st 1367
 
10.5%
ramp 1187
 
9.1%
av 995
 
7.7%
w 495
 
3.8%
to 274
 
2.1%
e 228
 
1.8%
broadway 177
 
1.4%
7 117
 
0.9%
Other values (434) 4379
33.7%
2023-11-08T23:11:24.324893image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8170
14.5%
a 7041
 
12.5%
o 3951
 
7.0%
R 3462
 
6.1%
t 2880
 
5.1%
d 2727
 
4.8%
w 2666
 
4.7%
y 2629
 
4.7%
n 1571
 
2.8%
S 1571
 
2.8%
Other values (57) 19765
35.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 34125
60.5%
Uppercase Letter 9717
 
17.2%
Space Separator 8170
 
14.5%
Decimal Number 2605
 
4.6%
Other Punctuation 1661
 
2.9%
Open Punctuation 87
 
0.2%
Dash Punctuation 45
 
0.1%
Close Punctuation 23
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 7041
20.6%
o 3951
11.6%
t 2880
8.4%
d 2727
 
8.0%
w 2666
 
7.8%
y 2629
 
7.7%
n 1571
 
4.6%
m 1397
 
4.1%
p 1376
 
4.0%
r 1303
 
3.8%
Other values (15) 6584
19.3%
Uppercase Letter
ValueCountFrequency (%)
R 3462
35.6%
S 1571
16.2%
A 1233
 
12.7%
W 853
 
8.8%
B 439
 
4.5%
E 346
 
3.6%
P 230
 
2.4%
C 180
 
1.9%
N 175
 
1.8%
F 171
 
1.8%
Other values (14) 1057
 
10.9%
Decimal Number
ValueCountFrequency (%)
1 471
18.1%
8 334
12.8%
7 318
12.2%
4 305
11.7%
2 277
10.6%
3 267
10.2%
5 212
8.1%
6 195
7.5%
9 146
 
5.6%
0 80
 
3.1%
Other Punctuation
ValueCountFrequency (%)
& 1470
88.5%
/ 136
 
8.2%
, 48
 
2.9%
. 7
 
0.4%
Space Separator
ValueCountFrequency (%)
8170
100.0%
Open Punctuation
ValueCountFrequency (%)
( 87
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 45
100.0%
Close Punctuation
ValueCountFrequency (%)
) 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 43842
77.7%
Common 12591
 
22.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 7041
16.1%
o 3951
 
9.0%
R 3462
 
7.9%
t 2880
 
6.6%
d 2727
 
6.2%
w 2666
 
6.1%
y 2629
 
6.0%
n 1571
 
3.6%
S 1571
 
3.6%
m 1397
 
3.2%
Other values (39) 13947
31.8%
Common
ValueCountFrequency (%)
8170
64.9%
& 1470
 
11.7%
1 471
 
3.7%
8 334
 
2.7%
7 318
 
2.5%
4 305
 
2.4%
2 277
 
2.2%
3 267
 
2.1%
5 212
 
1.7%
6 195
 
1.5%
Other values (8) 572
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56433
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8170
14.5%
a 7041
 
12.5%
o 3951
 
7.0%
R 3462
 
6.1%
t 2880
 
5.1%
d 2727
 
4.8%
w 2666
 
4.7%
y 2629
 
4.7%
n 1571
 
2.8%
S 1571
 
2.8%
Other values (57) 19765
35.0%
Distinct860
Distinct (%)5.4%
Missing2140241
Missing (%)99.3%
Memory size16.5 MiB
2023-11-08T23:11:24.574615image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length100
Median length99
Mean length16.791883
Min length3

Characters and Unicode

Total characters268519
Distinct characters68
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique237 ?
Unique (%)1.5%

Sample

1st rowMezzanine
2nd rowMezzanine
3rd rowStairway
4th rowPlatform
5th rowPlatform
ValueCountFrequency (%)
platform 5307
 
11.9%
exit 4346
 
9.7%
mezzanine 3578
 
8.0%
2307
 
5.2%
entrance 1713
 
3.8%
st 1463
 
3.3%
ave 1428
 
3.2%
stairway 958
 
2.1%
blvd 886
 
2.0%
expwy 790
 
1.8%
Other values (695) 21990
49.1%
2023-11-08T23:11:24.882933image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
28834
 
10.7%
a 19883
 
7.4%
t 18803
 
7.0%
e 17238
 
6.4%
n 16593
 
6.2%
r 13913
 
5.2%
i 12377
 
4.6%
o 9981
 
3.7%
l 9040
 
3.4%
E 8282
 
3.1%
Other values (58) 113575
42.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 173669
64.7%
Uppercase Letter 38932
 
14.5%
Space Separator 28834
 
10.7%
Decimal Number 12557
 
4.7%
Open Punctuation 5363
 
2.0%
Close Punctuation 5294
 
2.0%
Dash Punctuation 2980
 
1.1%
Other Punctuation 890
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 19883
11.4%
t 18803
10.8%
e 17238
9.9%
n 16593
 
9.6%
r 13913
 
8.0%
i 12377
 
7.1%
o 9981
 
5.7%
l 9040
 
5.2%
z 7281
 
4.2%
m 6147
 
3.5%
Other values (16) 42413
24.4%
Uppercase Letter
ValueCountFrequency (%)
E 8282
21.3%
P 6979
17.9%
M 4297
11.0%
S 3057
 
7.9%
B 2920
 
7.5%
A 2590
 
6.7%
W 1352
 
3.5%
R 1095
 
2.8%
C 1048
 
2.7%
I 1017
 
2.6%
Other values (15) 6295
16.2%
Decimal Number
ValueCountFrequency (%)
1 2473
19.7%
2 2174
17.3%
5 1250
10.0%
3 1234
9.8%
4 1095
8.7%
9 1043
8.3%
7 1036
8.3%
8 955
 
7.6%
6 815
 
6.5%
0 482
 
3.8%
Other Punctuation
ValueCountFrequency (%)
/ 730
82.0%
. 147
 
16.5%
, 13
 
1.5%
Space Separator
ValueCountFrequency (%)
28834
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5363
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5294
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2980
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 212601
79.2%
Common 55918
 
20.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 19883
 
9.4%
t 18803
 
8.8%
e 17238
 
8.1%
n 16593
 
7.8%
r 13913
 
6.5%
i 12377
 
5.8%
o 9981
 
4.7%
l 9040
 
4.3%
E 8282
 
3.9%
z 7281
 
3.4%
Other values (41) 79210
37.3%
Common
ValueCountFrequency (%)
28834
51.6%
( 5363
 
9.6%
) 5294
 
9.5%
- 2980
 
5.3%
1 2473
 
4.4%
2 2174
 
3.9%
5 1250
 
2.2%
3 1234
 
2.2%
4 1095
 
2.0%
9 1043
 
1.9%
Other values (7) 4178
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 268519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
28834
 
10.7%
a 19883
 
7.4%
t 18803
 
7.0%
e 17238
 
6.4%
n 16593
 
6.2%
r 13913
 
5.2%
i 12377
 
4.6%
o 9981
 
3.7%
l 9040
 
3.4%
E 8282
 
3.1%
Other values (58) 113575
42.3%

Latitude
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct464593
Distinct (%)21.9%
Missing33616
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean40.732729
Minimum40.498807
Maximum40.912869
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 MiB
2023-11-08T23:11:24.957662image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum40.498807
5-th percentile40.59741
Q140.672162
median40.726149
Q340.806275
95-th percentile40.868183
Maximum40.912869
Range0.41406209
Interquartile range (IQR)0.1341138

Descriptive statistics

Standard deviation0.084668591
Coefficient of variation (CV)0.0020786378
Kurtosis-0.82581567
Mean40.732729
Median Absolute Deviation (MAD)0.062202949
Skewness0.023693011
Sum86459942
Variance0.0071687704
MonotonicityNot monotonic
2023-11-08T23:11:25.014473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.72195913 8184
 
0.4%
40.71724957 3845
 
0.2%
40.7786329 2693
 
0.1%
40.8315271 2583
 
0.1%
40.68364303 2582
 
0.1%
40.89187242 2305
 
0.1%
40.65475302 2292
 
0.1%
40.6684778 2261
 
0.1%
40.81853516 1986
 
0.1%
40.64832049 1970
 
0.1%
Other values (464583) 2091915
97.0%
(Missing) 33616
 
1.6%
ValueCountFrequency (%)
40.4988067 1
 
< 0.1%
40.49892396 1
 
< 0.1%
40.49894885 1
 
< 0.1%
40.49905831 2
 
< 0.1%
40.49912214 1
 
< 0.1%
40.49927504 1
 
< 0.1%
40.49930796 3
 
< 0.1%
40.49937854 5
 
< 0.1%
40.49940396 1
 
< 0.1%
40.49940871 15
< 0.1%
ValueCountFrequency (%)
40.9128688 23
< 0.1%
40.91282765 1
 
< 0.1%
40.91246817 4
 
< 0.1%
40.91231946 17
< 0.1%
40.91230844 1
 
< 0.1%
40.91228642 6
 
< 0.1%
40.9122754 3
 
< 0.1%
40.91221958 1
 
< 0.1%
40.91221758 3
 
< 0.1%
40.9121894 1
 
< 0.1%

Longitude
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct464594
Distinct (%)21.9%
Missing33616
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean-73.92393
Minimum-74.254952
Maximum-73.700376
Zeros0
Zeros (%)0.0%
Negative2122616
Negative (%)98.4%
Memory size16.5 MiB
2023-11-08T23:11:25.109541image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-74.254952
5-th percentile-74.021496
Q1-73.970228
median-73.928211
Q3-73.87769
95-th percentile-73.79064
Maximum-73.700376
Range0.5545754
Interquartile range (IQR)0.092538509

Descriptive statistics

Standard deviation0.077910036
Coefficient of variation (CV)-0.0010539217
Kurtosis1.5823323
Mean-73.92393
Median Absolute Deviation (MAD)0.045738213
Skewness-0.32727814
Sum-1.5691212 × 108
Variance0.0060699737
MonotonicityNot monotonic
2023-11-08T23:11:25.179800image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-73.80969682 8184
 
0.4%
-73.80343341 3845
 
0.2%
-73.9625461 2693
 
0.1%
-73.92886303 2583
 
0.1%
-74.00030287 2582
 
0.1%
-73.86016845 2305
 
0.1%
-73.87323986 2292
 
0.1%
-73.84687385 2261
 
0.1%
-73.9413558 1986
 
0.1%
-73.78828125 1970
 
0.1%
Other values (464584) 2091915
97.0%
(Missing) 33616
 
1.6%
ValueCountFrequency (%)
-74.25495172 1
 
< 0.1%
-74.25473797 1
 
< 0.1%
-74.25473091 1
 
< 0.1%
-74.2546657 2
< 0.1%
-74.25462445 1
 
< 0.1%
-74.2545726 3
< 0.1%
-74.25437535 1
 
< 0.1%
-74.25422295 1
 
< 0.1%
-74.25394246 1
 
< 0.1%
-74.25376196 1
 
< 0.1%
ValueCountFrequency (%)
-73.70037632 4
< 0.1%
-73.70038355 2
 
< 0.1%
-73.70059685 2
 
< 0.1%
-73.70073668 2
 
< 0.1%
-73.70074293 6
< 0.1%
-73.70075063 2
 
< 0.1%
-73.70076037 1
 
< 0.1%
-73.7007611 1
 
< 0.1%
-73.70090285 1
 
< 0.1%
-73.70090631 2
 
< 0.1%

Location
Text

MISSING 

Distinct464601
Distinct (%)21.9%
Missing33616
Missing (%)1.6%
Memory size16.5 MiB
2023-11-08T23:11:25.756424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length90
Median length39
Mean length39.054151
Min length26

Characters and Unicode

Total characters82896966
Distinct characters32
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique215292 ?
Unique (%)10.1%

Sample

1st row(40.65665084455846, -73.90933142555113)
2nd row(40.59303241455525, -73.9572059782219)
3rd row(40.85844745053305, -73.92927888866892)
4th row(40.66889502741585, -73.93294405917439)
5th row(40.892021197823865, -73.86063833365542)
ValueCountFrequency (%)
40.72195913199264 8184
 
0.2%
73.80969682426189 8184
 
0.2%
40.71724956934783 3845
 
0.1%
73.80343340538089 3845
 
0.1%
40.77863290127972 2693
 
0.1%
73.96254609596876 2693
 
0.1%
40.8315271048226 2583
 
0.1%
73.92886303221528 2583
 
0.1%
40.68364302931577 2582
 
0.1%
74.00030286766025 2582
 
0.1%
Other values (929185) 4205469
99.1%
2023-11-08T23:11:26.354511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 8477235
10.2%
4 8061955
9.7%
3 7416968
8.9%
0 7346144
8.9%
9 6727450
8.1%
8 6711940
8.1%
6 6473622
7.8%
5 5831032
 
7.0%
2 5532653
 
6.7%
1 5459597
 
6.6%
Other values (22) 14858370
17.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68038596
82.1%
Other Punctuation 6367853
 
7.7%
Space Separator 2122629
 
2.6%
Open Punctuation 2122617
 
2.6%
Dash Punctuation 2122616
 
2.6%
Close Punctuation 2122616
 
2.6%
Lowercase Letter 34
 
< 0.1%
Control 4
 
< 0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 8
23.5%
e 6
17.6%
t 4
11.8%
s 4
11.8%
a 3
 
8.8%
o 2
 
5.9%
u 2
 
5.9%
n 2
 
5.9%
l 1
 
2.9%
g 1
 
2.9%
Decimal Number
ValueCountFrequency (%)
7 8477235
12.5%
4 8061955
11.8%
3 7416968
10.9%
0 7346144
10.8%
9 6727450
9.9%
8 6711940
9.9%
6 6473622
9.5%
5 5831032
8.6%
2 5532653
8.1%
1 5459597
8.0%
Other Punctuation
ValueCountFrequency (%)
. 4245232
66.7%
, 2122618
33.3%
: 3
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 2122616
> 99.9%
{ 1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 2122615
> 99.9%
} 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
2122629
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2122616
100.0%
Control
ValueCountFrequency (%)
4
100.0%
Uppercase Letter
ValueCountFrequency (%)
I 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 82896931
> 99.9%
Latin 35
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
7 8477235
10.2%
4 8061955
9.7%
3 7416968
8.9%
0 7346144
8.9%
9 6727450
8.1%
8 6711940
8.1%
6 6473622
7.8%
5 5831032
 
7.0%
2 5532653
 
6.7%
1 5459597
 
6.6%
Other values (10) 14858335
17.9%
Latin
ValueCountFrequency (%)
r 8
22.9%
e 6
17.1%
t 4
11.4%
s 4
11.4%
a 3
 
8.6%
o 2
 
5.7%
u 2
 
5.7%
n 2
 
5.7%
I 1
 
2.9%
l 1
 
2.9%
Other values (2) 2
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 82896966
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 8477235
10.2%
4 8061955
9.7%
3 7416968
8.9%
0 7346144
8.9%
9 6727450
8.1%
8 6711940
8.1%
6 6473622
7.8%
5 5831032
 
7.0%
2 5532653
 
6.7%
1 5459597
 
6.6%
Other values (22) 14858370
17.9%

Zip Codes
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct219
Distinct (%)< 0.1%
Missing42923
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean14643.099
Minimum10090
Maximum26001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 MiB
2023-11-08T23:11:26.429188image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum10090
5-th percentile10700
Q111723
median13516
Q317213
95-th percentile24016
Maximum26001
Range15911
Interquartile range (IQR)5490

Descriptive statistics

Standard deviation3623.2429
Coefficient of variation (CV)0.24743689
Kurtosis1.0124105
Mean14643.099
Median Absolute Deviation (MAD)2241
Skewness1.2031337
Sum3.0945393 × 1010
Variance13127889
MonotonicityNot monotonic
2023-11-08T23:11:26.658182image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17215 32340
 
1.5%
13510 31767
 
1.5%
15310 29850
 
1.4%
16865 29373
 
1.4%
11605 28885
 
1.3%
10930 28482
 
1.3%
11606 28198
 
1.3%
17613 28124
 
1.3%
10935 27728
 
1.3%
10934 27033
 
1.3%
Other values (209) 1821529
84.5%
(Missing) 42923
 
2.0%
ValueCountFrequency (%)
10090 5941
0.3%
10091 1058
 
< 0.1%
10092 3326
0.2%
10093 147
 
< 0.1%
10094 111
 
< 0.1%
10095 88
 
< 0.1%
10096 17
 
< 0.1%
10097 9
 
< 0.1%
10098 278
 
< 0.1%
10099 8282
0.4%
ValueCountFrequency (%)
26001 31
 
< 0.1%
25293 1
 
< 0.1%
24894 47
 
< 0.1%
24672 62
 
< 0.1%
24671 5639
 
0.3%
24670 11599
0.5%
24669 11894
0.6%
24668 10629
0.5%
24340 15449
0.7%
24339 2202
 
0.1%

Community Districts
Real number (ℝ)

MISSING 

Distinct71
Distinct (%)< 0.1%
Missing34126
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean36.751201
Minimum1
Maximum71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 MiB
2023-11-08T23:11:26.717862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q118
median39
Q354
95-th percentile69
Maximum71
Range70
Interquartile range (IQR)36

Descriptive statistics

Standard deviation20.595558
Coefficient of variation (CV)0.56040502
Kurtosis-1.2188162
Mean36.751201
Median Absolute Deviation (MAD)18
Skewness-0.062213299
Sum77989944
Variance424.17699
MonotonicityNot monotonic
2023-11-08T23:11:26.772401image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47 64061
 
3.0%
36 56640
 
2.6%
45 54424
 
2.5%
41 53205
 
2.5%
54 52869
 
2.5%
50 51614
 
2.4%
39 50908
 
2.4%
20 49686
 
2.3%
18 48246
 
2.2%
24 46785
 
2.2%
Other values (61) 1593668
73.9%
ValueCountFrequency (%)
1 36663
1.7%
2 32297
1.5%
3 1025
 
< 0.1%
4 35128
1.6%
5 34029
1.6%
6 43464
2.0%
7 27612
1.3%
8 15076
 
0.7%
9 28607
1.3%
10 29454
1.4%
ValueCountFrequency (%)
71 23283
1.1%
70 41348
1.9%
69 44968
2.1%
68 46556
2.2%
67 1885
 
0.1%
66 30648
1.4%
65 33611
1.6%
64 2028
 
0.1%
63 33993
1.6%
62 41013
1.9%

Borough Boundaries
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing34131
Missing (%)1.6%
Memory size16.5 MiB
2.0
659106 
3.0
523524 
4.0
454134 
5.0
398214 
1.0
87123 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6366303
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row4.0
4th row2.0
5th row5.0

Common Values

ValueCountFrequency (%)
2.0 659106
30.6%
3.0 523524
24.3%
4.0 454134
21.1%
5.0 398214
18.5%
1.0 87123
 
4.0%
(Missing) 34131
 
1.6%

Length

2023-11-08T23:11:26.822033image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-08T23:11:26.872099image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 659106
31.1%
3.0 523524
24.7%
4.0 454134
21.4%
5.0 398214
18.8%
1.0 87123
 
4.1%

Most occurring characters

ValueCountFrequency (%)
. 2122101
33.3%
0 2122101
33.3%
2 659106
 
10.4%
3 523524
 
8.2%
4 454134
 
7.1%
5 398214
 
6.3%
1 87123
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4244202
66.7%
Other Punctuation 2122101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2122101
50.0%
2 659106
 
15.5%
3 523524
 
12.3%
4 454134
 
10.7%
5 398214
 
9.4%
1 87123
 
2.1%
Other Punctuation
ValueCountFrequency (%)
. 2122101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6366303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 2122101
33.3%
0 2122101
33.3%
2 659106
 
10.4%
3 523524
 
8.2%
4 454134
 
7.1%
5 398214
 
6.3%
1 87123
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6366303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 2122101
33.3%
0 2122101
33.3%
2 659106
 
10.4%
3 523524
 
8.2%
4 454134
 
7.1%
5 398214
 
6.3%
1 87123
 
1.4%

City Council Districts
Real number (ℝ)

MISSING 

Distinct51
Distinct (%)< 0.1%
Missing34126
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean27.761464
Minimum1
Maximum51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 MiB
2023-11-08T23:11:26.924028image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q115
median29
Q339
95-th percentile49
Maximum51
Range50
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.257871
Coefficient of variation (CV)0.51358497
Kurtosis-1.1221083
Mean27.761464
Median Absolute Deviation (MAD)12
Skewness-0.16992589
Sum58912770
Variance203.28688
MonotonicityNot monotonic
2023-11-08T23:11:26.979991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 65746
 
3.0%
36 62669
 
2.9%
38 61304
 
2.8%
29 57564
 
2.7%
39 56422
 
2.6%
22 55546
 
2.6%
48 54350
 
2.5%
30 51818
 
2.4%
46 50545
 
2.3%
35 49740
 
2.3%
Other values (41) 1556402
72.2%
ValueCountFrequency (%)
1 22182
 
1.0%
2 36251
1.7%
3 26683
1.2%
4 38522
1.8%
5 28735
1.3%
6 33665
1.6%
7 37835
1.8%
8 29324
1.4%
9 28076
1.3%
10 65746
3.0%
ValueCountFrequency (%)
51 42403
2.0%
50 45401
2.1%
49 45656
2.1%
48 54350
2.5%
47 29216
1.4%
46 50545
2.3%
45 36184
1.7%
44 37328
1.7%
43 49658
2.3%
42 46905
2.2%

Police Precincts
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct77
Distinct (%)< 0.1%
Missing34126
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean41.66148
Minimum1
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 MiB
2023-11-08T23:11:27.035678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q126
median41
Q361
95-th percentile73
Maximum77
Range76
Interquartile range (IQR)35

Descriptive statistics

Standard deviation21.013409
Coefficient of variation (CV)0.5043846
Kurtosis-1.0818418
Mean41.66148
Median Absolute Deviation (MAD)18
Skewness-0.10779936
Sum88410077
Variance441.56336
MonotonicityNot monotonic
2023-11-08T23:11:27.091752image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47 54464
 
2.5%
62 53105
 
2.5%
72 51744
 
2.4%
27 51618
 
2.4%
34 46909
 
2.2%
67 45040
 
2.1%
30 44117
 
2.0%
29 43480
 
2.0%
43 43424
 
2.0%
64 41045
 
1.9%
Other values (67) 1647160
76.4%
ValueCountFrequency (%)
1 21266
1.0%
2 14188
0.7%
3 19122
0.9%
4 12592
 
0.6%
5 23465
1.1%
6 15585
0.7%
7 20817
1.0%
8 19072
0.9%
9 12639
 
0.6%
10 32463
1.5%
ValueCountFrequency (%)
77 18695
 
0.9%
76 24614
1.1%
75 20001
 
0.9%
74 23813
1.1%
73 34530
1.6%
72 51744
2.4%
71 29001
1.3%
70 22661
1.1%
69 21319
1.0%
68 33421
1.5%

Request Closing Time
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing163693
Missing (%)7.6%
Memory size16.5 MiB

Interactions

2023-11-08T23:10:34.235420image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:13.676395image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:15.691323image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:17.636767image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:19.664587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:21.781758image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:23.899220image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:25.957039image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:28.124758image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:30.183513image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:32.216972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:34.424470image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:13.865125image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:15.867806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:17.812550image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:19.852727image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:21.966231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:24.081814image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:26.142735image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:28.308553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:30.364225image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:32.398444image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:34.592153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:14.036097image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:16.041975image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:17.976277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:20.022095image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:22.141217image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:24.250918image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:26.409991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:28.484582image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:30.532493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:32.566415image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:34.776049image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:14.222622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:16.213250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:18.143036image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:20.201866image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:22.329397image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:24.436527image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:26.593167image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:28.670556image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:30.717772image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:32.749861image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:34.972160image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:14.409252image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:16.387495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:18.315691image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:20.415631image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:22.517668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:24.624639image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:26.798015image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:28.859229image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:30.906640image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:32.938801image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:35.164563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:14.593708image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:16.560740image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:18.636107image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:20.610948image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:22.716973image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:24.807969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:26.985340image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:29.048463image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:31.097721image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:33.126359image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:35.348702image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:14.771835image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:16.729912image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:18.799528image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:20.794011image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:22.921359image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:24.995600image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:27.164535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:29.231724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:31.278722image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:33.306526image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:35.536360image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:14.951190image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:16.916452image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:18.964416image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:20.982252image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:23.107463image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:25.195525image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:27.348897image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:29.411349image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:31.463726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:33.490029image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:35.860209image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:15.137576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:17.095363image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:19.134250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:21.195661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:23.313919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:25.385606image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:27.541798image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:29.597549image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:31.648122image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:33.674849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:36.050055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:15.320308image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:17.276564image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:19.301327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:21.391663image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:23.503241image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:25.573016image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:27.728026image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:29.784564image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:31.832613image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:33.855634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:36.234212image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:15.504007image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:17.446633image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:19.465520image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:21.578871image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:23.695614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:25.756823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:27.923433image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:29.977362image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:32.019555image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T23:10:34.039911image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-11-08T23:11:27.153915image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Unique KeyIncident ZipBBLX Coordinate (State Plane)Y Coordinate (State Plane)LatitudeLongitudeZip CodesCommunity DistrictsCity Council DistrictsPolice PrecinctsAgencyAgency NameAddress TypeFacility TypeStatusBoroughOpen Data Channel TypePark BoroughVehicle TypeTaxi Company BoroughBorough Boundaries
Unique Key1.0000.001-0.006-0.001-0.003-0.003-0.0010.0020.0110.008-0.0040.0580.0580.0150.0250.1020.0380.0240.0380.1180.0000.019
Incident Zip0.0011.0000.8010.533-0.456-0.4560.5320.7140.175-0.0340.7430.0240.0240.0191.0000.0030.1920.0000.1921.0001.0000.001
BBL-0.0060.8011.0000.356-0.576-0.5760.3550.5300.047-0.1910.9170.1090.1090.1040.2330.0400.8930.0650.8930.1220.8561.000
X Coordinate (State Plane)-0.0010.5330.3561.0000.3390.3381.0000.2000.300-0.0270.3260.0990.0990.0320.1600.0330.6090.0670.6090.0850.6180.680
Y Coordinate (State Plane)-0.003-0.456-0.5760.3391.0001.0000.340-0.5340.0690.018-0.4500.1120.1120.0300.2530.0390.5720.0620.5720.1050.5230.638
Latitude-0.003-0.456-0.5760.3381.0001.0000.339-0.5340.0690.019-0.4500.1120.1120.0300.2530.0390.5720.0620.5720.1050.5230.638
Longitude-0.0010.5320.3551.0000.3400.3391.0000.1990.300-0.0270.3260.1000.1000.0320.1610.0330.6080.0670.6080.0850.6180.680
Zip Codes0.0020.7140.5300.200-0.534-0.5340.1991.0000.1510.1410.5060.1030.1030.0300.1920.0340.6780.0600.6780.0000.6070.758
Community Districts0.0110.1750.0470.3000.0690.0690.3000.1511.0000.2040.1070.0970.0970.0210.2480.0300.3530.0610.3530.1110.3570.395
City Council Districts0.008-0.034-0.191-0.0270.0180.019-0.0270.1410.2041.000-0.1800.0880.0880.0370.1110.0260.3830.0560.3830.0800.4160.428
Police Precincts-0.0040.7430.9170.326-0.450-0.4500.3260.5060.107-0.1801.0000.1390.1390.0360.2650.0420.7470.0730.7470.0480.6810.836
Agency0.0580.0240.1090.0990.1120.1120.1000.1030.0970.0880.1391.0001.0000.2700.7090.2850.1490.4530.1491.0001.0000.165
Agency Name0.0580.0240.1090.0990.1120.1120.1000.1030.0970.0880.1391.0001.0000.2700.7090.2850.1490.4530.1491.0001.0000.165
Address Type0.0150.0190.1040.0320.0300.0300.0320.0300.0210.0370.0360.2700.2701.0000.1770.1160.0370.1680.0370.0600.1580.028
Facility Type0.0251.0000.2330.1600.2530.2530.1610.1920.2480.1110.2650.7090.7090.1771.0000.0790.2000.7010.2000.0000.0000.201
Status0.1020.0030.0400.0330.0390.0390.0330.0340.0300.0260.0420.2850.2850.1160.0791.0000.0410.1400.0410.0000.0000.045
Borough0.0380.1920.8930.6090.5720.5720.6080.6780.3530.3830.7470.1490.1490.0370.2000.0411.0000.0611.0000.1240.8560.998
Open Data Channel Type0.0240.0000.0650.0670.0620.0620.0670.0600.0610.0560.0730.4530.4530.1680.7010.1400.0611.0000.0610.0000.1530.061
Park Borough0.0380.1920.8930.6090.5720.5720.6080.6780.3530.3830.7470.1490.1490.0370.2000.0411.0000.0611.0000.1240.8560.998
Vehicle Type0.1181.0000.1220.0850.1050.1050.0850.0000.1110.0800.0481.0001.0000.0600.0000.0000.1240.0000.1241.0000.0610.120
Taxi Company Borough0.0001.0000.8560.6180.5230.5230.6180.6070.3570.4160.6811.0001.0000.1580.0000.0000.8560.1530.8560.0611.0000.862
Borough Boundaries0.0190.0011.0000.6800.6380.6380.6800.7580.3950.4280.8360.1650.1650.0280.2010.0450.9980.0610.9980.1200.8621.000

Missing values

2023-11-08T23:10:40.359920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-08T23:10:48.201801image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-11-08T23:11:09.322688image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unique KeyCreated DateClosed DateAgencyAgency NameComplaint TypeDescriptorLocation TypeIncident ZipIncident AddressStreet NameCross Street 1Cross Street 2Intersection Street 1Intersection Street 2Address TypeCityLandmarkFacility TypeStatusDue DateResolution DescriptionResolution Action Updated DateCommunity BoardBBLBoroughX Coordinate (State Plane)Y Coordinate (State Plane)Open Data Channel TypePark Facility NamePark BoroughVehicle TypeTaxi Company BoroughTaxi Pick Up LocationBridge Highway NameBridge Highway DirectionRoad RampBridge Highway SegmentLatitudeLongitudeLocationZip CodesCommunity DistrictsBorough BoundariesCity Council DistrictsPolice PrecinctsRequest Closing Time
0593480052023-11-07 12:00:00NaTDSNYDepartment of SanitationDerelict VehiclesDerelict VehiclesStreet11212.0585 BRISTOL STREETBRISTOL STREETLOTT AVENUEHEGEMAN AVENUENoneNoneADDRESSBROOKLYNNoneDSNY GarageOpenNaTIf the abandoned vehicle meets the criteria to be classified as a derelict (i.e. junk) the Department of Sanitation (DSNY) will investigate and tag the vehicle within three business days.2023-11-07 12:00:0016 BROOKLYN3.036240e+09BROOKLYN1009407.0178525.0PHONEUnspecifiedBROOKLYNNoneNoneNoneNoneNoneNoneNone40.656651-73.909331(40.65665084455846, -73.90933142555113)17614.055.02.025.046.0NaT
1593480062023-11-07 12:00:00NaTDSNYDepartment of SanitationDerelict VehiclesDerelict VehiclesStreet11229.02362 EAST 13 STREETEAST 13 STREETGRAVESEND NECK ROADAVENUE XNoneNoneADDRESSBROOKLYNNoneNoneOpenNaTIf the abandoned vehicle meets the criteria to be classified as a derelict (i.e. junk) the Department of Sanitation (DSNY) will investigate and tag the vehicle within three business days.2023-11-07 12:00:0015 BROOKLYN3.073978e+09BROOKLYN996135.0155337.0PHONEUnspecifiedBROOKLYNNoneNoneNoneNoneNoneNoneNone40.593032-73.957206(40.59303241455525, -73.9572059782219)13512.032.02.015.036.0NaT
2593521592023-11-07 12:00:00NaTDSNYDepartment of SanitationDerelict VehiclesDerelict VehiclesStreet10040.034 HILLSIDE AVENUEHILLSIDE AVENUEBOGARDUS PLACEELLWOOD STREETNoneNoneADDRESSNEW YORKNoneNoneOpenNaTIf the abandoned vehicle meets the criteria to be classified as a derelict (i.e. junk) the Department of Sanitation (DSNY) will investigate and tag the vehicle within three business days.2023-11-07 12:00:0012 MANHATTAN1.021710e+09MANHATTAN1003813.0252041.0PHONEUnspecifiedMANHATTANNoneNoneNoneNoneNoneNoneNone40.858447-73.929279(40.85844745053305, -73.92927888866892)13098.047.04.039.022.0NaT
3593463102023-11-07 02:21:07NaTDOTDepartment of TransportationStreet ConditionPotholeNone11105.0CRESCENT STREETCRESCENT STREET23 AVENUEDITMARS BOULEVARDNoneNoneBLOCKFACEQUEENSNoneN/AOpenNaTThe Department of Transportation referred this complaint to the appropriate Maintenance Unit for repair.2023-11-07 02:21:0801 QUEENSNaNQUEENSNaNNaNUNKNOWNUnspecifiedQUEENSNoneNoneNoneNoneNoneNoneNoneNaNNaNNoneNaNNaNNaNNaNNaNNaT
4593535902023-11-07 02:07:50NaTNYPDNew York City Police DepartmentPanhandlingN/ASubwayNaNNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneIn ProgressNaTNone2023-11-07 02:27:43Unspecified BROOKLYNNaNBROOKLYN1002852.0182980.0MOBILEUnspecifiedBROOKLYNNoneNoneNone4NoneNoneMezzanine40.668895-73.932944(40.66889502741585, -73.93294405917439)17615.017.02.048.044.0NaT
5593519612023-11-07 02:07:24NaTNYPDNew York City Police DepartmentBlocked DrivewayNo AccessStreet/Sidewalk10466.0637 EAST 230 STREETEAST 230 STREETCARPENTER AVENUELOWERRE PLACECARPENTER AVENUELOWERRE PLACEADDRESSBRONXEAST 230 STREETNoneIn ProgressNaTNoneNaT12 BRONX2.048330e+09BRONX1022781.0264296.0PHONEUnspecifiedBRONXNoneNoneNoneNoneNoneNoneNone40.892021-73.860638(40.892021197823865, -73.86063833365542)11275.029.05.02.030.0NaT
6593479392023-11-07 02:07:17NaTNYPDNew York City Police DepartmentIllegal ParkingPosted Parking Sign ViolationStreet/Sidewalk11214.080 BAY 50 STREETBAY 50 STREETWEST 16 STREETPRIVATE CATANZARO SQUAREWEST 16 STREETPRIVATE CATANZARO SQUAREADDRESSBROOKLYNBAY 50 STREETNoneIn ProgressNaTNone2023-11-07 02:20:5313 BROOKLYN3.069170e+09BROOKLYN988275.0153113.0MOBILEUnspecifiedBROOKLYNNoneNoneNoneNoneNoneNoneNone40.586935-73.985509(40.586935033893944, -73.98550860707033)17616.021.02.045.035.0NaT
7593438002023-11-07 02:07:04NaTNYPDNew York City Police DepartmentIllegal ParkingBlocked HydrantStreet/Sidewalk11235.02422 EAST 29 STREETEAST 29 STREETAVENUE XAVENUE YAVENUE XAVENUE YADDRESSBROOKLYNEAST 29 STREETNoneIn ProgressNaTNoneNaT15 BROOKLYN3.074221e+09BROOKLYN1000491.0155341.0ONLINEUnspecifiedBROOKLYNNoneNoneNoneNoneNoneNoneNone40.593036-73.941521(40.59303648228758, -73.94152143256076)13826.032.02.015.036.0NaT
8593451742023-11-07 02:06:08NaTNYPDNew York City Police DepartmentNoise - ResidentialLoud Music/PartyResidential Building/House10031.0514 WEST 136 STREETWEST 136 STREETAMSTERDAM AVENUEBROADWAYAMSTERDAM AVENUEBROADWAYADDRESSNEW YORKWEST 136 STREETNoneIn ProgressNaTNone2023-11-07 02:33:3409 MANHATTAN1.019880e+09MANHATTAN997300.0238040.0ONLINEUnspecifiedMANHATTANNoneNoneNoneNoneNoneNoneNone40.820031-73.952851(40.82003081301524, -73.952850909447)12428.037.04.023.019.0NaT
9593493352023-11-07 02:04:27NaTNYPDNew York City Police DepartmentNoise - Street/SidewalkLoud Music/PartyStreet/Sidewalk10456.01164 SHERIDAN AVENUESHERIDAN AVENUEMCCLELLAN STREETEAST 167 STREETMCCLELLAN STREETEAST 167 STREETADDRESSBRONXSHERIDAN AVENUENoneIn ProgressNaTNoneNaT04 BRONX2.024560e+09BRONX1007148.0242841.0ONLINEUnspecifiedBRONXNoneNoneNoneNoneNoneNoneNone40.833188-73.917254(40.83318814574537, -73.91725413909168)10934.050.05.042.027.0NaT
Unique KeyCreated DateClosed DateAgencyAgency NameComplaint TypeDescriptorLocation TypeIncident ZipIncident AddressStreet NameCross Street 1Cross Street 2Intersection Street 1Intersection Street 2Address TypeCityLandmarkFacility TypeStatusDue DateResolution DescriptionResolution Action Updated DateCommunity BoardBBLBoroughX Coordinate (State Plane)Y Coordinate (State Plane)Open Data Channel TypePark Facility NamePark BoroughVehicle TypeTaxi Company BoroughTaxi Pick Up LocationBridge Highway NameBridge Highway DirectionRoad RampBridge Highway SegmentLatitudeLongitudeLocationZip CodesCommunity DistrictsBorough BoundariesCity Council DistrictsPolice PrecinctsRequest Closing Time
2156222570236312023-03-12 18:53:182023-03-15 09:49:35HPDDepartment of Housing Preservation and DevelopmentDOOR/WINDOWWINDOW FRAMERESIDENTIAL BUILDING11374.063-25 SAUNDERS STREETSAUNDERS STREETNoneNoneNoneNoneADDRESSREGO PARKNoneNoneClosedNaTThe Department of Housing Preservation and Development inspected the following conditions. No violations were issued. The complaint has been closed.2023-03-15 00:00:0006 QUEENS4.030800e+09QUEENS1021930.0205172.0ONLINEUnspecifiedQUEENSNoneNoneNoneNoneNoneNoneNone40.729746-73.864048(40.729746170156055, -73.86404817380075)14785.040.03.028.070.02 days 14:56:17
2156223570254262023-03-12 18:53:182023-03-15 09:49:35HPDDepartment of Housing Preservation and DevelopmentHEAT/HOT WATERAPARTMENT ONLYRESIDENTIAL BUILDING11374.063-25 SAUNDERS STREETSAUNDERS STREETNoneNoneNoneNoneADDRESSREGO PARKNoneNoneClosedNaTThe Department of Housing Preservation and Development inspected the following conditions. No violations were issued. The complaint has been closed.2023-03-15 00:00:0006 QUEENS4.030800e+09QUEENS1021930.0205172.0ONLINEUnspecifiedQUEENSNoneNoneNoneNoneNoneNoneNone40.729746-73.864048(40.729746170156055, -73.86404817380075)14785.040.03.028.070.02 days 14:56:17
2156224570274852023-03-12 18:53:002023-03-12 22:56:00DEPDepartment of Environmental ProtectionWater SystemHydrant Running (WC3)None11104.050-44 42 STREET42 STREET50 AVE51 AVENoneNoneADDRESSSUNNYSIDENoneNoneClosedNaTThe Department of Environmental Protection determined that this complaint is a duplicate of a previously filed complaint. The original complaint is being addressed.2023-03-12 22:56:0002 QUEENS4.001810e+09QUEENS1005377.0207746.0PHONEUnspecifiedQUEENSNoneNoneNoneNoneNoneNoneNone40.736866-73.923764(40.73686618999776, -73.92376432151327)16861.053.03.033.066.00 days 04:03:00
2156225570222392023-03-12 18:53:002023-03-12 21:05:00DEPDepartment of Environmental ProtectionWater SystemHydrant Running (WC3)None10003.016 WASHINGTON PLACEWASHINGTON PLACEMERCER STGREENE STNoneNoneADDRESSNEW YORKNoneNoneClosedNaTThe Department of Environmental Protection investigated this complaint and shut the running hydrant.2023-03-12 21:05:0002 MANHATTAN1.005460e+09MANHATTAN985580.0205131.0MOBILEUnspecifiedMANHATTANNoneNoneNoneNoneNoneNoneNone40.729714-73.995201(40.729713791524404, -73.99520128002949)11724.057.04.032.03.00 days 02:12:00
2156226570244682023-03-12 18:52:572023-03-13 12:05:42DCADepartment of Consumer AffairsConsumer ComplaintRetail StoreBusiness11214.08222 18 AVENUE18 AVENUE82 STREET83 STREET82 STREET83 STREETADDRESSBROOKLYN18 AVENUENoneClosedNaTUnfortunately, the behavior that you complained about does not violate any law or rule. As a result, no city agency has the jurisdiction to act on the matter.2023-03-13 12:05:5111 BROOKLYN3.063140e+09BROOKLYN984100.0161140.0PHONEUnspecifiedBROOKLYNNoneNoneNoneNoneNoneNoneNone40.608968-74.000540(40.608968439263236, -74.00054023014746)17616.01.02.044.037.00 days 17:12:45
2156227570265512023-03-12 18:52:442023-03-13 03:39:42NYPDNew York City Police DepartmentIllegal ParkingBlocked HydrantStreet/Sidewalk11385.078-36 79 PLACE79 PLACE78 AVENUEMYRTLE AVENUE78 AVENUEMYRTLE AVENUEADDRESSRIDGEWOOD79 PLACENoneClosedNaTThe Police Department responded and upon arrival those responsible for the condition were gone.2023-03-13 03:39:4705 QUEENS4.038280e+09QUEENS1020202.0195810.0MOBILEUnspecifiedQUEENSNoneNoneNoneNoneNoneNoneNone40.704057-73.870333(40.70405694530993, -73.8703328992707)15310.054.03.034.062.00 days 08:46:58
2156228570268292023-03-12 18:52:302023-03-12 22:53:52NYPDNew York City Police DepartmentNoise - ResidentialLoud Music/PartyResidential Building/House11102.030-07 NEWTOWN AVENUENEWTOWN AVENUE30 STREET31 STREET30 STREET31 STREETADDRESSASTORIANEWTOWN AVENUENoneClosedNaTThe Police Department responded to the complaint and with the information available observed no evidence of the violation at that time.2023-03-12 22:53:5701 QUEENS4.005988e+09QUEENS1006094.0219178.0ONLINEUnspecifiedQUEENSNoneNoneNoneNoneNoneNoneNone40.768242-73.921140(40.76824237904741, -73.92113992853868)16859.039.03.04.072.00 days 04:01:22
2156229570222262023-03-12 18:52:082023-03-12 21:10:51NYPDNew York City Police DepartmentIllegal ParkingBlocked HydrantStreet/Sidewalk11218.0390 OCEAN PARKWAYOCEAN PARKWAYAVENUE CCORTELYOU ROADAVENUE CCORTELYOU ROADADDRESSBROOKLYNOCEAN PARKWAYNoneClosedNaTThe Police Department responded and upon arrival those responsible for the condition were gone.2023-03-12 21:10:5412 BROOKLYN3.053740e+09BROOKLYN991638.0172258.0MOBILEUnspecifiedBROOKLYNNoneNoneNoneNoneNoneNoneNone40.639482-73.973380(40.63948194101334, -73.97337969679486)17620.02.02.027.039.00 days 02:18:43
2156230570262552023-03-12 18:52:002023-03-14 08:35:54HPDDepartment of Housing Preservation and DevelopmentHEAT/HOT WATERENTIRE BUILDINGRESIDENTIAL BUILDING11106.031-35 CRESCENT STREETCRESCENT STREETNoneNoneNoneNoneADDRESSASTORIANoneNoneClosedNaTThe complaint you filed is a duplicate of a condition already reported by another tenant for a building-wide condition. The original complaint is still open. HPD may attempt to contact you to verify the correction of the condition or may conduct an inspection of your unit if the original complainant is not available for verification.2023-03-14 00:00:0001 QUEENS4.005790e+09QUEENS1004421.0217880.0MOBILEUnspecifiedQUEENSNoneNoneNoneNoneNoneNoneNone40.764684-73.927184(40.76468368198577, -73.92718359841146)16863.039.03.04.072.01 days 13:43:54
2156231570252022023-03-12 18:51:462023-03-12 18:59:55NYPDNew York City Police DepartmentNoise - ResidentialBanging/PoundingResidential Building/House10468.0233 LANDING ROADLANDING ROADCEDAR AVENUEMAJOR DEEGAN EXPRESSWAYCEDAR AVENUEMAJOR DEEGAN EXPRESSWAYADDRESSBRONXLANDING ROADNoneClosedNaTThe Police Department reviewed your complaint and provided additional information below.2023-03-12 19:00:0007 BRONX2.032368e+09BRONX1008841.0253488.0ONLINEUnspecifiedBRONXNoneNoneNoneNoneNoneNoneNone40.862406-73.911097(40.86240645467484, -73.9110{\n error : true,\n message : Internal error,\n status : 500\n}NaNNaNNaNNaNNaN0 days 00:08:09